diff --git a/_common/cudaq/execute.py b/_common/cudaq/execute.py
index 4a27f093..535fa6f2 100644
--- a/_common/cudaq/execute.py
+++ b/_common/cudaq/execute.py
@@ -5,7 +5,7 @@
 # you may not use this file except in compliance with the License.
 # You may obtain a copy of the License at
 #
-#     http:#www.apache.org/licenses/LICENSE-2.0
+#	  http:#www.apache.org/licenses/LICENSE-2.0
 #
 # Unless required by applicable law or agreed to in writing, software
 # distributed under the License is distributed on an "AS IS" BASIS,
@@ -23,17 +23,32 @@
 # execution, so they can be aggregated and presented to the user.
 #
 
+import os, sys
 import time
 import copy
+
+# import metrics module relative to top level of package
+current_dir = os.path.dirname(os.path.abspath(__file__))
+
+common_dir = os.path.abspath(os.path.join(current_dir, ".."))
+#sys.path = [common_dir] + [p for p in sys.path if p != common_dir]
+
+top_dir = os.path.abspath(os.path.join(common_dir, ".."))
+sys.path = [top_dir] + [p for p in sys.path if p != top_dir]
+
+#print(sys.path)
+
+# DEVNOTE: for some reason, this does not work - why?
+#import _common.metrics as metrics
+
+# instead, need to include the common directory in path and do this
 import metrics
 
+# import the CUDA-Q package
 import cudaq
 
 verbose = False
 
-###import cirq
-###backend = cirq.Simulator()      # Use Cirq Simulator by default
-
 #noise = 'DEFAULT'
 noise=None
 
@@ -45,7 +60,8 @@
 active_circuits = { }
 result_handler = None
 
-device=None
+# save the executing device (backend_id) here
+device = None
 
 #######################
 # SUPPORTING CLASSES
@@ -53,214 +69,276 @@
 # class BenchmarkResult is used as a compatible return object from execution
 class BenchmarkResult(object):
 
-    def __init__(self, cq_result):
-        super().__init__()
-        self.cq_result = cq_result
+	def __init__(self, cq_result):
+		super().__init__()
+		self.cq_result = cq_result
 
-    def get_counts(self, qc=0):
-        counts = self.cq_result
-        return counts
+	def get_counts(self, qc=0):
+		counts = self.cq_result
+		return counts
 
 # Special Job object class to hold job information for custom executors
 class Job:
-    local_job = True
-    unique_job_id = 1001
-    executor_result = None
-    
-    def __init__(self):
-        Job.unique_job_id = Job.unique_job_id + 1
-        self.this_job_id = Job.unique_job_id      
-        
-    def job_id(self):
-        return self.this_job_id
-    
-    def status(self):
-        return JobStatus.DONE
-        
-    def result(self):
-        return self.executor_result
+	local_job = True
+	unique_job_id = 1001
+	executor_result = None
+	
+	def __init__(self):
+		Job.unique_job_id = Job.unique_job_id + 1
+		self.this_job_id = Job.unique_job_id	  
+		
+	def job_id(self):
+		return self.this_job_id
+	
+	def status(self):
+		return JobStatus.DONE
+		
+	def result(self):
+		return self.executor_result
 
 #######################
 # 
 
 # Initialize the execution module, with a custom result handler
 def init_execution (handler):
-    global batched_circuits, result_handler
-    batched_circuits.clear()
-    active_circuits.clear()
-    result_handler = handler
-    
-    # create an informative device name
-    # this should be move to set_execution_target method later
-    device_name = "simulator"
-    metrics.set_plot_subtitle(f"Device = {device_name}")
+	global batched_circuits, result_handler
+	batched_circuits.clear()
+	active_circuits.clear()
+	result_handler = handler
+	
+	# create an informative device name
+	# this should be move to set_execution_target method later
+	device_name = "simulator"
+	metrics.set_plot_subtitle(f"Device = {device_name}")
 
 
 # Set the backend for execution
-def set_execution_target(backend_id='cudaq_simulator', provider_backend=None,
-        hub=None, group=None, project=None, exec_options=None,
-        context=None):
-    """
-    Set the backend execution target.
-    :param backend_id:  device name. List of available devices depends on the provider
-    :provider_backend: a custom backend object created and passed in, use backend_id as identifier
-    
-    example usage.
-    set_execution_target(backend_id='aqt_qasm_simulator', 
-                        provider_backende=aqt.backends.aqt_qasm_simulator)
-    """
-    global backend   
-    
-    # default to cudaq_simulator if None passed in
-    if backend_id == None:
-        backend_id="cudaq_simulator"
-        
-    # if a custom provider backend is given, use it ...
-    if provider_backend != None:
-        backend = provider_backend
-    '''   
-    # otherwise test for simulator
-    elif backend_id == 'simulator':
-        backend = cirq.Simulator()
-       
-    # nothing else is supported yet, default to simulator       
-    else:
-        print(f"ERROR: Unknown backend_id: {backend_id}, defaulting to Cirq Simulator")
-        backend = cirq.Simulator()
-        backend_id = "simulator"
-    '''
-    # create an informative device name
-    device_name = backend_id
-    metrics.set_plot_subtitle(f"Device = {device_name}")
-
-
+def set_execution_target(backend_id=None, provider_backend=None,
+		hub=None, group=None, project=None, exec_options=None,
+		context=None):
+	"""
+	Set the backend execution target.
+	:param backend_id:	device name. List of available devices depends on the provider
+	:provider_backend: a custom backend object created and passed in, use backend_id as identifier
+	
+	example usage.
+	set_execution_target(backend_id='aqt_qasm_simulator', 
+						provider_backende=aqt.backends.aqt_qasm_simulator)
+	"""
+	global backend	 
+	
+	# in case anyone uses a name similar to that used in other APIs
+	if backend_id == "cudaq_simulator":
+		backend_id = None
+	
+	# default to nvidia execution engine if None passed in
+	if backend_id == None:
+		backend_id="nvidia"
+		
+	# if a custom provider backend is given, set it; currently unused
+	if provider_backend != None:
+		backend = provider_backend
+	
+	# now set the execution target to the given backend_id
+	cudaq.set_target(backend_id)
+	
+	# create an informative device name used by the metrics module
+	device_name = backend_id
+	metrics.set_plot_subtitle(f"Device = {device_name}")
+	
+	print(f"... configure execution for target backend_id = {backend_id}")
+
+# CUDA-Q supports several different models of noise. In this default
+# case, we use the modeling of depolarization noise only. This
+# depolarization will result in the qubit state decaying into a mix
+# of the basis states, |0> and |1>, with a user provided probability.
+# DEVNOTE: there are no two qubit error settings here
+def set_default_noise_model():
+	global noise
+	
+	# We will begin by defining an empty noise model that we will add
+	# our depolarization channel to.
+	noise = cudaq.NoiseModel()
+
+	# We define a depolarization channel setting the probability
+	# of the qubit state being scrambled to `1.0`.
+	depolarization = cudaq.DepolarizationChannel(0.04)
+	
+	phase_flip = cudaq.PhaseFlipChannel(0.2)
+
+	for i in range(30):
+		noise.add_channel('x', [i], depolarization)
+		noise.add_channel('y', [i], depolarization)
+		noise.add_channel('z', [i], depolarization)
+		
+		noise.add_channel('h', [i], depolarization)
+		
+		# consider adding this
+		#noise.add_channel('x', [i], phase_flip)
+		#noise.add_channel('y', [i], phase_flip)
+		#noise.add_channel('z', [i], phase_flip)
+		
+		noise.add_channel('rx', [i], depolarization)
+		noise.add_channel('ry', [i], depolarization)
+		noise.add_channel('rz', [i], depolarization)
+	
+	if verbose:
+		print(f"  ... just set DEFAULT noise model to: {noise}")
+
+# Configure execution to use the given noise model
 def set_noise_model(noise_model = None):
-    # see reference on NoiseModel here https://quantumai.google/cirq/noise
-    global noise
-    noise = noise_model
+	
+	global noise
+	noise = noise_model
+	
+	if verbose:
+		print(f"... just set noise model to: {noise}")
 
 
 # Submit circuit for execution
 # This version executes immediately and calls the result handler
 def submit_circuit (qc, group_id, circuit_id, shots=100):
 
-    # store circuit in array with submission time and circuit info
-    batched_circuits.append(
-        { "qc": qc, "group": str(group_id), "circuit": str(circuit_id),
-            "submit_time": time.time(), "shots": shots }
-    )
-    #print("... submit circuit - ", str(batched_circuits[len(batched_circuits)-1]))
-    
-    # DEVNOTE: execute immediately for now, so that we don't accumulate elapsed time while in queue
-    execute_circuits()
-    
-    
+	# store circuit in array with submission time and circuit info
+	batched_circuits.append(
+		{ "qc": qc, "group": str(group_id), "circuit": str(circuit_id),
+			"submit_time": time.time(), "shots": shots }
+	)
+	#print("... submit circuit - ", str(batched_circuits[len(batched_circuits)-1]))
+	
+	# DEVNOTE: execute immediately for now, so that we don't accumulate elapsed time while in queue
+	execute_circuits()
+	
+	
 # Launch execution of all batched circuits
 def execute_circuits ():
-    for batched_circuit in batched_circuits:
-        execute_circuit(batched_circuit)
-    batched_circuits.clear()
-    
+	for batched_circuit in batched_circuits:
+		execute_circuit(batched_circuit)
+	batched_circuits.clear()
+	
 # Launch execution of one batched circuit
 def execute_circuit (batched_circuit):
-    if verbose:
-        print(f'... execute_circuit({batched_circuit["group"]}, {batched_circuit["circuit"]})')
-
-    active_circuit = copy.copy(batched_circuit)
-    active_circuit["launch_time"] = time.time()
-    
-    num_shots = batched_circuit["shots"]
-    
-    # Initiate execution 
-    circuit = batched_circuit["qc"]
-    '''
-    if type(noise) == str and noise == "DEFAULT":
-        # depolarizing noise on all qubits
-        circuit = circuit.with_noise(cirq.depolarize(0.05))
-    elif noise is not None:
-        # otherwise we expect it to be a NoiseModel
-        # see documentation at https://quantumai.google/cirq/noise
-        circuit = circuit.with_noise(noise)
-    
-    # experimental, for testing AQT device
-    if device != None:
-        circuit.device=device
-        device.validate_circuit(circuit)
-
-    job.result = backend.run(circuit, repetitions=shots)
-    '''
-    
-    # create a pseudo-job to perform metrics processing upon return
-    job = Job()
-    
-    # draw the circuit, but only for debugging
-    # print(cudaq.draw(circuit[0], *circuit[1]))
-    
-    ts = time.time()
-    # call sample() on circuit with its list of arguments
-    result = cudaq.sample(circuit[0], *circuit[1], shots_count=num_shots)
-    exec_time = time.time() - ts
-    
-    # store the result object on the job for processing in job_complete
-    job.executor_result = result 
-    job.exec_time = exec_time
-    
-    if verbose:
-        print(f"... result = {len(result)} {result}")
-        ''' for debugging, a better way to see the counts, as the type of result is something Quake
-        for key, val in result.items():
-            print(f"... {key}:{val}")
-        '''
-        print(f"... register names = {result.register_names}")
-        print(result.dump())
-        #print(f"... register dump = {result.get_register_counts('__global__').dump()}")
-        #result.get_register_counts("b1").dump()
-        #print(result.get_sequential_data())
-        
-    # put job into the active circuits with circuit info
-    active_circuits[job] = active_circuit
-    #print("... active_circuit = ", str(active_circuit))
-    
-    ##############
-    # Here we complete the job immediately 
-    job_complete(job)
+	if verbose:
+		print(f'... execute_circuit({batched_circuit["group"]}, {batched_circuit["circuit"]})')
+
+	active_circuit = copy.copy(batched_circuit)
+	active_circuit["launch_time"] = time.time()
+	
+	num_shots = batched_circuit["shots"]
+	
+	# Initiate execution 
+	circuit = batched_circuit["qc"]
+	
+	# create a pseudo-job to perform metrics processing upon return
+	job = Job()
+	
+	# draw the circuit, but only for debugging
+	# print(cudaq.draw(circuit[0], *circuit[1]))
+	
+	ts = time.time()
+	
+	# call sample() on circuit with its list of arguments
+	if verbose: print(f"... during exec, noise model is: {noise}")
+	if noise is None:
+		if verbose: print("... executing without noise")
+		result = cudaq.sample(circuit[0], *circuit[1], shots_count=num_shots)
+	else:
+		if verbose: print("... executing WITH noise")
+		result = cudaq.sample(circuit[0], *circuit[1], shots_count=num_shots, noise_model=noise)
+	
+	# control results print at benchmark level
+	#if verbose: print(result)
+		
+	exec_time = time.time() - ts
+	
+	# store the result object on the job for processing in job_complete
+	job.executor_result = result 
+	job.exec_time = exec_time
+	
+	if verbose:
+		print(f"... result = {len(result)} {result}")
+		''' for debugging, a better way to see the counts, as the type of result is something Quake
+		for key, val in result.items():
+			print(f"... {key}:{val}")
+		'''
+		print(f"... register names = {result.register_names}")
+		print(result.dump())
+		#print(f"... register dump = {result.get_register_counts('__global__').dump()}")
+		#result.get_register_counts("b1").dump()
+		#print(result.get_sequential_data())
+		
+	# put job into the active circuits with circuit info
+	active_circuits[job] = active_circuit
+	#print("... active_circuit = ", str(active_circuit))
+	
+	# ***********************************
+	
+	# store circuit dimensional metrics
+	# DEVNOTE: this is not accurate; it is provided so the volumetric plots show something
+	
+	# compute depth and gate counts based on number of qubits
+	qc_size = int(active_circuit["group"])
+	qc_depth = 4 * pow(qc_size, 2)
+
+	qc_xi = 0.5
+
+	qc_n2q = int(qc_depth * 0.75)
+	qc_tr_depth = qc_depth
+	qc_tr_size = qc_size
+	qc_tr_xi = qc_xi
+	qc_tr_n2q = qc_n2q
+	
+	# store circuit dimensional metrics
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'depth', qc_depth)
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'size', qc_size)
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'xi', qc_xi)
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'n2q', qc_n2q)
+
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'tr_depth', qc_tr_depth)
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'tr_size', qc_tr_size)
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'tr_xi', qc_tr_xi)
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'tr_n2q', qc_tr_n2q)
+	
+	##############
+	# Here we complete the job immediately 
+	job_complete(job)
 
 # klunky way to know the last group executed 
 last_group = None 
 
 # Process a completed job
 def job_complete (job):
-    active_circuit = active_circuits[job]
-        
-    # get job result (DEVNOTE: this might be different for diff targets)
-    cq_result = job.result()
-    
-    # create a compatible Result object to return to the caller
-    result = BenchmarkResult(cq_result)
-    
-    # counts = result.get_counts(qc)
-    # print("Total counts are:", counts)
-
-    # store time metrics
-    metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'elapsed_time',
-        time.time() - active_circuit["submit_time"])
-       
-    metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'exec_time',
-        job.exec_time)
-    
-    # If a handler has been established, invoke it here with result object
-    if result_handler:
-        result_handler(active_circuit["qc"],
-            result, active_circuit["group"], active_circuit["circuit"], active_circuit["shots"])
-    
-    # DEVNOTE: a hack to store the last group identifier for use in the job management functions
-    group = active_circuit["group"]
-    global last_group
-    last_group = group
-                           
-    del active_circuits[job]
+	active_circuit = active_circuits[job]
+		
+	# get job result (DEVNOTE: this might be different for diff targets)
+	cq_result = job.result()
+	
+	# create a compatible Result object to return to the caller
+	result = BenchmarkResult(cq_result)
+	
+	# counts = result.get_counts(qc)
+	# print("Total counts are:", counts)
+
+	# store time metrics
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'elapsed_time',
+		time.time() - active_circuit["submit_time"])
+	   
+	metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'exec_time',
+		job.exec_time)
+	
+	# If a handler has been established, invoke it here with result object
+	if result_handler:
+		result_handler(active_circuit["qc"],
+			result, active_circuit["group"], active_circuit["circuit"], active_circuit["shots"])
+	
+	# DEVNOTE: a hack to store the last group identifier for use in the job management functions
+	group = active_circuit["group"]
+	global last_group
+	last_group = group
+						   
+	del active_circuits[job]
  
-        
+		
 ######################################################################
 # JOB MANAGEMENT METHODS
 
@@ -268,7 +346,7 @@ def job_complete (job):
 # and completion processing of circuits that are submitted for execution. 
 
 # Throttle the execution of active and batched jobs.
-# Wait for active jobs to complete.  As each job completes,
+# Wait for active jobs to complete.	 As each job completes,
 # check if there are any batched circuits waiting to be executed.
 # If so, execute them, removing them from the batch.
 # Execute the user-supplied completion handler to allow user to 
@@ -277,48 +355,48 @@ def job_complete (job):
 # otherwise continue to wait for additional active circuits to complete.
 
 def throttle_execution(completion_handler=metrics.finalize_group):
-    #logger.info('Entering throttle_execution')
-
-    if verbose:
-        print(f"... throttling execution, active={len(active_circuits)}, batched={len(batched_circuits)}")
-    
-    # DEVNOTE: execution is currently synchronous, so force execution of any batched circuits
-    execute_circuits()
-
-    global last_group
-    group = last_group
-    
-    # call completion handler with the group id
-    if completion_handler != None:
-        completion_handler(group)
-                
-    '''
-    # check and sleep if not complete
-    done = False
-    pollcount = 0
-    while not done:
-    
-        # check if any jobs complete
-        check_jobs(completion_handler)
-
-        # return only when all jobs complete
-        if len(batched_circuits) < 1:
-            break
-            
-        # delay a bit, increasing the delay periodically 
-        sleeptime = 0.25
-        if pollcount > 6: sleeptime = 0.5
-        if pollcount > 60: sleeptime = 1.0
-        time.sleep(sleeptime)
-        
-        pollcount += 1
-    
-    if verbose:
-        if pollcount > 0: print("") 
-        #print(f"... throttling execution(2), active={len(active_circuits)}, batched={len(batched_circuits)}")
-    '''
-    
-    
+	#logger.info('Entering throttle_execution')
+
+	if verbose:
+		print(f"... throttling execution, active={len(active_circuits)}, batched={len(batched_circuits)}")
+	
+	# DEVNOTE: execution is currently synchronous, so force execution of any batched circuits
+	execute_circuits()
+
+	global last_group
+	group = last_group
+	
+	# call completion handler with the group id
+	if completion_handler != None:
+		completion_handler(group)
+				
+	'''  DEVNOTE: this or something similar could be used later for throtttling
+	# check and sleep if not complete
+	done = False
+	pollcount = 0
+	while not done:
+	
+		# check if any jobs complete
+		check_jobs(completion_handler)
+
+		# return only when all jobs complete
+		if len(batched_circuits) < 1:
+			break
+			
+		# delay a bit, increasing the delay periodically 
+		sleeptime = 0.25
+		if pollcount > 6: sleeptime = 0.5
+		if pollcount > 60: sleeptime = 1.0
+		time.sleep(sleeptime)
+		
+		pollcount += 1
+	
+	if verbose:
+		if pollcount > 0: print("") 
+		#print(f"... throttling execution(2), active={len(active_circuits)}, batched={len(batched_circuits)}")
+	'''
+	
+	
 # Wait for all active and batched circuits to complete.
 # Execute the user-supplied completion handler to allow user to 
 # check if a group of circuits has been completed and report on results.
@@ -327,60 +405,60 @@ def throttle_execution(completion_handler=metrics.finalize_group):
 
 def finalize_execution(completion_handler=metrics.finalize_group, report_end=True):
 
-    if verbose:
-        print("... finalize_execution")
-        
-    # DEVNOTE: execution is currently synchronous, so force execution of any batched circuits
-    execute_circuits()
-    
-    '''
-    # check and sleep if not complete
-    done = False
-    pollcount = 0
-    while not done:
-    
-        # check if any jobs complete
-        check_jobs(completion_handler)
-
-        # return only when all jobs complete
-        if len(active_circuits) < 1:
-            break
-            
-        # delay a bit, increasing the delay periodically 
-        sleeptime = 0.10                        # was 0.25
-        if pollcount > 6: sleeptime = 0.20      # 0.5
-        if pollcount > 60: sleeptime = 0.5      # 1.0
-        time.sleep(sleeptime)
-        
-        pollcount += 1
-    
-    if verbose:
-        if pollcount > 0: print("")
-    '''
-    # indicate we are done collecting metrics (called once at end of app)
-    if report_end:
-        metrics.end_metrics()
-    '''   
-    # also, close any active session at end of the app
-    global session
-    if report_end and session != None:
-        if verbose:
-            print(f"... closing active session: {session_count}\n")
-        
-        session.close()
-        session = None
-    '''
+	if verbose:
+		print("... finalize_execution")
+		
+	# DEVNOTE: execution is currently synchronous, so force execution of any batched circuits
+	execute_circuits()
+	
+	'''   DEVNOTE: this or something similar could be used later for throtttling
+	# check and sleep if not complete
+	done = False
+	pollcount = 0
+	while not done:
+	
+		# check if any jobs complete
+		check_jobs(completion_handler)
+
+		# return only when all jobs complete
+		if len(active_circuits) < 1:
+			break
+			
+		# delay a bit, increasing the delay periodically 
+		sleeptime = 0.10						# was 0.25
+		if pollcount > 6: sleeptime = 0.20		# 0.5
+		if pollcount > 60: sleeptime = 0.5		# 1.0
+		time.sleep(sleeptime)
+		
+		pollcount += 1
+	
+	if verbose:
+		if pollcount > 0: print("")
+	'''
+	# indicate we are done collecting metrics (called once at end of app)
+	if report_end:
+		metrics.end_metrics()
+	'''	  
+	# also, close any active session at end of the app
+	global session
+	if report_end and session != None:
+		if verbose:
+			print(f"... closing active session: {session_count}\n")
+		
+		session.close()
+		session = None
+	'''
  
 # Wait for all executions to complete
 def wait_for_completion():
 
-    # check and sleep if not complete
-    pass
-    
-    # return only when all circuits complete
+	# check and sleep if not complete
+	pass
+	
+	# return only when all circuits complete
 
 
 # Test circuit execution
 def test_execution():
-    pass
-    
\ No newline at end of file
+	pass
+	
\ No newline at end of file
diff --git a/_common/custom/custom_qiskit_noise_model.py b/_common/custom/custom_qiskit_noise_model.py
index 53e0a895..34102a79 100644
--- a/_common/custom/custom_qiskit_noise_model.py
+++ b/_common/custom/custom_qiskit_noise_model.py
@@ -5,8 +5,9 @@
 # Note: this custom definition is the same as the default noise model provided
 # The code is provided here for users to copy to their own file and customize
 
-from qiskit.providers.aer.noise import NoiseModel, ReadoutError
-from qiskit.providers.aer.noise import depolarizing_error, reset_error
+# Noise Model imports
+from qiskit_aer.noise import NoiseModel, ReadoutError
+from qiskit_aer.noise import depolarizing_error, reset_error
 
 def my_noise_model():
     
diff --git a/_common/qiskit/execute.py b/_common/qiskit/execute.py
index 7981ec5c..1a63c65b 100644
--- a/_common/qiskit/execute.py
+++ b/_common/qiskit/execute.py
@@ -147,9 +147,12 @@ def __init__(self, qiskit_result):
         self.metadata = qiskit_result.metadata
 
     def get_counts(self, qc=0):
-        counts= self.qiskit_result.quasi_dists[0].binary_probabilities()
-        for key in counts.keys():
-            counts[key] = int(counts[key] * self.qiskit_result.metadata[0]['shots'])        
+        # counts= self.qiskit_result.quasi_dists[0].binary_probabilities()
+        # for key in counts.keys():
+        #     counts[key] = int(counts[key] * self.qiskit_result.metadata[0]['shots'])
+        qc_index = 0 # this should point to the index of the circuit in a pub
+        bitvals = next(iter(self.qiskit_result[qc_index].data.values()))
+        counts = bitvals.get_counts()
         return counts
 
 # Special Job object class to hold job information for custom executors
@@ -250,6 +253,7 @@ def set_execution_target(backend_id='qasm_simulator',
                         provider_name='Quantinuum')
     """
     global backend
+    global sampler
     global session
     global use_sessions
     global session_count
@@ -291,12 +295,16 @@ def set_execution_target(backend_id='qasm_simulator',
     # handle Statevector simulator specially
     elif backend_id == 'statevector_simulator':
         backend = Aer.get_backend("statevector_simulator")
-        
+    
+    elif backend_id == "statevector_sampler":
+        from qiskit.primitives import StatevectorSampler
+        sampler = StatevectorSampler()
+
     # handle 'fake' backends here
     elif 'fake' in backend_id:
         backend = getattr(
             importlib.import_module(
-                f'qiskit.providers.fake_provider.backends.{backend_id.split("_")[-1]}.{backend_id}'
+                f'qiskit_ibm_runtime.fake_provider.backends.{backend_id.split("_")[-1]}.{backend_id}'
             ),
             backend_id.title().replace('_', '')
         )
@@ -306,7 +314,6 @@ def set_execution_target(backend_id='qasm_simulator',
     # otherwise use the given providername or backend_id to find the backend
     else:
         global service
-        global sampler
     
         # if provider_module name and provider_name are provided, obtain a custom provider
         if provider_module_name and provider_name:  
@@ -375,7 +382,7 @@ def set_execution_target(backend_id='qasm_simulator',
 
                 # if use sessions, setup runtime service, Session, and Sampler
                 if use_sessions:
-                    from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Session, Options
+                    from qiskit_ibm_runtime import QiskitRuntimeService, Session, Options, SamplerV2 as Sampler
                     
                     service = QiskitRuntimeService()
                     session_count += 1
@@ -385,8 +392,8 @@ def set_execution_target(backend_id='qasm_simulator',
                     
                     # get Sampler resilience level and transpiler optimization level from exec_options
                     options = Options()
-                    options.resilience_level = exec_options.get("resilience_level", 1)
-                    options.optimization_level = exec_options.get("optimization_level", 3)
+                    # options.resilience_level = exec_options.get("resilience_level", 1)
+                    # options.optimization_level = exec_options.get("optimization_level", 3)
                     
                     # special handling for ibmq_qasm_simulator to set noise model
                     if backend_id == "ibmq_qasm_simulator":
@@ -423,14 +430,13 @@ def set_execution_target(backend_id='qasm_simulator',
         ###############################
         # otherwise, assume the backend_id is given only and assume it is IBM Cloud device
         else:
-            from qiskit_ibm_runtime import QiskitRuntimeService, Sampler, Session, Options
+            from qiskit_ibm_runtime import QiskitRuntimeService, Session, SamplerOptions as Options, SamplerV2 as Sampler
             
             # create the Runtime Service object
             service = QiskitRuntimeService()
-            
             # obtain a backend from the service
             backend = service.backend(backend_id)
-               
+
             # DEVNOTE: here we assume if the sessions flag is set, we use Sampler
             # however, we may want to add a use_sampler option so that we can separate these
             
@@ -449,8 +455,8 @@ def set_execution_target(backend_id='qasm_simulator',
                 
                 # get Sampler resilience level and transpiler optimization level from exec_options
                 options = Options()
-                options.resilience_level = exec_options.get("resilience_level", 1)
-                options.optimization_level = exec_options.get("optimization_level", 3)
+                # options.resilience_level = exec_options.get("resilience_level", 1)
+                # options.optimization_level = exec_options.get("optimization_level", 3)
                 
                 # special handling for ibmq_qasm_simulator to set noise model
                 if backend_id == "ibmq_qasm_simulator":
@@ -675,7 +681,7 @@ def execute_circuit(circuit):
 
             #************************************************
             # Initiate execution (with noise if specified and this is a simulator backend)
-            if this_noise is not None and not use_sessions and backend_name.endswith("qasm_simulator"):
+            if this_noise is not None and not sampler and backend_name.endswith("qasm_simulator"):
                 logger.info(f"Performing noisy simulation, shots = {shots}")
                 
                 # if the noise model has associated QV value, copy it to metrics module for plotting
@@ -750,8 +756,9 @@ def execute_circuit(circuit):
                 logger.info(f'Running trans_qc, shots={shots}')
                 st = time.time() 
 
-                if use_sessions:
-                    job = sampler.run(trans_qc, shots=shots, **backend_exec_options_copy)
+                if sampler:
+                    # turn input into pub-like
+                    job = sampler.run([trans_qc], shots=shots)
                 else:
                     job = backend.run(trans_qc, shots=shots, **backend_exec_options_copy)
 
@@ -1106,7 +1113,7 @@ def job_complete(job):
     # get job result (DEVNOTE: this might be different for diff targets)
     result = None
         
-    if job.status() == JobStatus.DONE:
+    if job.status() == JobStatus.DONE or job.status() == 'DONE':
         result = job.result()
         # print("... result = ", str(result))
 
@@ -1128,13 +1135,18 @@ def job_complete(job):
         # if we are using sessions, structure of result object is different;
         # use a BenchmarkResult object to hold session result and provide a get_counts()
         # that returns counts to the benchmarks in the same form as without sessions
-        if use_sessions:
+        if sampler:
             result = BenchmarkResult(result)
             #counts = result.get_counts()
             
-            actual_shots = result.metadata[0]['shots']
-            result_obj = result.metadata[0]
-            results_obj = result.metadata[0]
+            # actual_shots = result.metadata[0]['shots']
+            # get the name of the classical register
+            # TODO: need to rewrite to allow for submit multiple circuits in one job
+            # get DataBin associated with the classical register
+            bitvals = next(iter(result.qiskit_result[0].data.values()))
+            actual_shots = bitvals.num_shots
+            result_obj = result.metadata # not sure how to update to be V2 compatible
+            results_obj = result.metadata
         else:
             result_obj = result.to_dict()
             results_obj = result.to_dict()['results'][0]
@@ -1169,6 +1181,10 @@ def job_complete(job):
         elif "time_taken" in results_obj:
             exec_time = results_obj["time_taken"]
         
+        elif 'execution' in result_obj:
+            # read execution time for the first circuit
+            exec_time = result_obj['execution']['execution_spans'][0].duration
+
         # override the initial value with exec_time returned from successful execution
         metrics.store_metric(active_circuit["group"], active_circuit["circuit"], 'exec_time', exec_time)
         
@@ -1273,7 +1289,7 @@ def process_step_times(job, result, active_circuit):
         
         if verbose:
             print(f"... job.metrics() = {job.metrics()}")
-            print(f"... job.result().metadata[0] = {result.metadata[0]}")
+            print(f"... job.result().metadata[0] = {result.metadata}")
 
         # occasionally, these metrics come back as None, so try to use them
         try:
@@ -1494,7 +1510,7 @@ def check_jobs(completion_handler=None):
             if hasattr(job, "error_message"):
                 print(f"    job = {job.job_id()}  {job.error_message()}")
 
-        if status == JobStatus.DONE or status == JobStatus.CANCELLED or status == JobStatus.ERROR:
+        if status == JobStatus.DONE or status == JobStatus.CANCELLED or status == JobStatus.ERROR or status == 'DONE' or status =='CANCELLED' or status == 'ERROR':
             #if verbose: print("Job status is ", job.status() )
             
             active_circuit = active_circuits[job]
diff --git a/amplitude-estimation/qiskit/ae_benchmark.py b/amplitude-estimation/qiskit/ae_benchmark.py
index 46c06615..47064d0c 100644
--- a/amplitude-estimation/qiskit/ae_benchmark.py
+++ b/amplitude-estimation/qiskit/ae_benchmark.py
@@ -228,7 +228,7 @@ def a_from_s_int(s_int, num_counting_qubits):
 # Execute program with default parameters
 def run(min_qubits=3, max_qubits=8, skip_qubits=1, max_circuits=3, num_shots=100,
         num_state_qubits=1, # default, not exposed to users
-        backend_id='qasm_simulator', provider_backend=None,
+        backend_id=None, provider_backend=None,
         hub="ibm-q", group="open", project="main", exec_options=None,
         context=None):
 
diff --git a/benchmarks-cudaq.ipynb b/benchmarks-cudaq.ipynb
index b88ab546..cd402c97 100644
--- a/benchmarks-cudaq.ipynb
+++ b/benchmarks-cudaq.ipynb
@@ -5,7 +5,7 @@
    "metadata": {},
    "source": [
     "### QED-C Application-Oriented Benchmarks - CUDA Quantum Version\n",
-    "The notebook contains a suite of application-oriented benchmarks for the cudaq API.\n",
+    "The notebook contains a suite of application-oriented benchmarks for the CUDA Quantum API.\n",
     "Configure and run the cell below with the desired execution settings.\n",
     "Then execute the remaining cells, each containing one benchmark program."
    ]
@@ -16,21 +16,38 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "# Specify the parameters for the benchmark\n",
     "min_qubits=2\n",
-    "max_qubits=8\n",
+    "max_qubits=30\n",
     "skip_qubits=1\n",
     "max_circuits=1\n",
     "num_shots=1000\n",
     "\n",
-    "backend_id=\"cudaq_simulator\"\n",
-    "#backend_id=\"statevector_simulator\"\n",
-    "\n",
+    "# Set these default parameters\n",
     "hub=\"\"; group=\"\"; project=\"\"\n",
     "provider_backend = None\n",
     "exec_options = {}\n",
     "\n",
-    "# For CUDSA Quantum, specify this API option\n",
-    "api=\"cudaq\"\n"
+    "# For CUDA Quantum, specify this API option\n",
+    "api=\"cudaq\"\n",
+    "\n",
+    "# Select the backend on which the benchmark program will execute\n",
+    "backend_id=\"nvidia\"\n",
+    "#backend_id=\"density-matrix-cpu\"\n",
+    "\n",
+    "# OPTIONS: \n",
+    "import sys\n",
+    "sys.path.insert(1, \"_common\")\n",
+    "sys.path.insert(1, \"_common/cudaq\")\n",
+    "\n",
+    "# # Uncomment these lines to add a suffix to your backend_id and data/image files\n",
+    "# import metrics\n",
+    "# metrics.data_suffix = \"-mysystem\"\n",
+    "\n",
+    "# # Uncomment these lines to add a default noise model for execution\n",
+    "# import execute\n",
+    "# #execute.set_noise_model(noise_model = None)      # for no noise\n",
+    "# execute.set_default_noise_model()                # for simple default noise model\n"
    ]
   },
   {
@@ -50,71 +67,1014 @@
      "output_type": "stream",
      "text": [
       "Bernstein-Vazirani (1) Benchmark Program - Qiskit\n",
-      "... execution starting at Aug 20, 2024 02:06:30 UTC\n",
+      "... execution starting at Sep 29, 2024 05:41:51 UTC\n",
+      "... configure execution for target backend_id = nvidia\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 3\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.062, 0.062 secs\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 3 qubit group = 36, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 3 qubit group = 36, 0.5, 27.0\n",
+      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.158, 0.158 secs\n",
       "Average Hellinger, Normalized Fidelity for the 3 qubit group = 1.0, 1.0\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 4\n",
       "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 4 qubit group = 64, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 4 qubit group = 64, 0.5, 48.0\n",
       "Average Creation, Elapsed, Execution Time for the 4 qubit group = 0.0, 0.002, 0.002 secs\n",
       "Average Hellinger, Normalized Fidelity for the 4 qubit group = 1.0, 1.0\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 5\n",
       "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 5 qubit group = 100, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 5 qubit group = 100, 0.5, 75.0\n",
       "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.002, 0.002 secs\n",
       "Average Hellinger, Normalized Fidelity for the 5 qubit group = 1.0, 1.0\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 6\n",
       "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 6 qubit group = 144, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 6 qubit group = 144, 0.5, 108.0\n",
       "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.002, 0.002 secs\n",
       "Average Hellinger, Normalized Fidelity for the 6 qubit group = 1.0, 1.0\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 7\n",
       "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 7 qubit group = 196, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 7 qubit group = 196, 0.5, 147.0\n",
       "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.002, 0.002 secs\n",
       "Average Hellinger, Normalized Fidelity for the 7 qubit group = 1.0, 1.0\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 8\n",
       "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 8 qubit group = 256, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 8 qubit group = 256, 0.5, 192.0\n",
+      "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 8 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 9\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 9 qubit group = 324, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 9 qubit group = 324, 0.5, 243.0\n",
+      "Average Creation, Elapsed, Execution Time for the 9 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 9 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 10\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 10 qubit group = 400, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 10 qubit group = 400, 0.5, 300.0\n",
+      "Average Creation, Elapsed, Execution Time for the 10 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 10 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 11\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 11 qubit group = 484, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 11 qubit group = 484, 0.5, 363.0\n",
+      "Average Creation, Elapsed, Execution Time for the 11 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 11 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 12\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 12 qubit group = 576, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 12 qubit group = 576, 0.5, 432.0\n",
+      "Average Creation, Elapsed, Execution Time for the 12 qubit group = 0.0, 0.007, 0.007 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 12 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 13\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 13 qubit group = 676, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 13 qubit group = 676, 0.5, 507.0\n",
+      "Average Creation, Elapsed, Execution Time for the 13 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 13 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 14\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 14 qubit group = 784, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 14 qubit group = 784, 0.5, 588.0\n",
+      "Average Creation, Elapsed, Execution Time for the 14 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 14 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 15\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 15 qubit group = 900, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 15 qubit group = 900, 0.5, 675.0\n",
+      "Average Creation, Elapsed, Execution Time for the 15 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 15 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 16\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 16 qubit group = 1024, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 16 qubit group = 1024, 0.5, 768.0\n",
+      "Average Creation, Elapsed, Execution Time for the 16 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 16 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 17\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 17 qubit group = 1156, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 17 qubit group = 1156, 0.5, 867.0\n",
+      "Average Creation, Elapsed, Execution Time for the 17 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 17 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 18\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 18 qubit group = 1296, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 18 qubit group = 1296, 0.5, 972.0\n",
+      "Average Creation, Elapsed, Execution Time for the 18 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 18 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 19\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 19 qubit group = 1444, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 19 qubit group = 1444, 0.5, 1083.0\n",
+      "Average Creation, Elapsed, Execution Time for the 19 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 19 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 20\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 20 qubit group = 1600, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 20 qubit group = 1600, 0.5, 1200.0\n",
+      "Average Creation, Elapsed, Execution Time for the 20 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 20 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 21\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 21 qubit group = 1764, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 21 qubit group = 1764, 0.5, 1323.0\n",
+      "Average Creation, Elapsed, Execution Time for the 21 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 21 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 22\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 22 qubit group = 1936, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 22 qubit group = 1936, 0.5, 1452.0\n",
+      "Average Creation, Elapsed, Execution Time for the 22 qubit group = 0.0, 0.007, 0.007 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 22 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 23\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 23 qubit group = 2116, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 23 qubit group = 2116, 0.5, 1587.0\n",
+      "Average Creation, Elapsed, Execution Time for the 23 qubit group = 0.0, 0.006, 0.006 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 23 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 24\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 24 qubit group = 2304, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 24 qubit group = 2304, 0.5, 1728.0\n",
+      "Average Creation, Elapsed, Execution Time for the 24 qubit group = 0.0, 0.012, 0.012 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 24 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 25\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 25 qubit group = 2500, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 25 qubit group = 2500, 0.5, 1875.0\n",
+      "Average Creation, Elapsed, Execution Time for the 25 qubit group = 0.0, 0.02, 0.02 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 25 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 26\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 26 qubit group = 2704, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 26 qubit group = 2704, 0.5, 2028.0\n",
+      "Average Creation, Elapsed, Execution Time for the 26 qubit group = 0.0, 0.04, 0.04 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 26 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 27\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 27 qubit group = 2916, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 27 qubit group = 2916, 0.5, 2187.0\n",
+      "Average Creation, Elapsed, Execution Time for the 27 qubit group = 0.0, 0.081, 0.081 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 27 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 28\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 28 qubit group = 3136, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 28 qubit group = 3136, 0.5, 2352.0\n",
+      "Average Creation, Elapsed, Execution Time for the 28 qubit group = 0.0, 0.153, 0.153 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 28 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 29\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 29 qubit group = 3364, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 29 qubit group = 3364, 0.5, 2523.0\n",
+      "Average Creation, Elapsed, Execution Time for the 29 qubit group = 0.0, 0.299, 0.299 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 29 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 30\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 30 qubit group = 3600, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 30 qubit group = 3600, 0.5, 2700.0\n",
+      "Average Creation, Elapsed, Execution Time for the 30 qubit group = 0.0, 0.593, 0.593 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 30 qubit group = 1.0, 1.0\n",
+      "\n",
+      "... execution complete at Sep 29, 2024 05:41:52 UTC in 1.497 secs\n",
+      "\n",
+      "Sample Circuit:\n",
+      "     ╭───╮                    ╭───╮\n",
+      "q0 : ┤ h ├─────────────────●──┤ h ├\n",
+      "     ├───┤                 │  ├───┤\n",
+      "q1 : ┤ h ├─────────────────┼──┤ h ├\n",
+      "     ├───┤                 │  ├───┤\n",
+      "q2 : ┤ h ├─────────────────┼──┤ h ├\n",
+      "     ├───┤                 │  ├───┤\n",
+      "q3 : ┤ h ├────────────●────┼──┤ h ├\n",
+      "     ├───┤            │    │  ├───┤\n",
+      "q4 : ┤ h ├───────●────┼────┼──┤ h ├\n",
+      "     ├───┤╭───╮╭─┴─╮╭─┴─╮╭─┴─╮╰───╯\n",
+      "q5 : ┤ h ├┤ z ├┤ x ├┤ x ├┤ x ├─────\n",
+      "     ╰───╯╰───╯╰───╯╰───╯╰───╯     \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x1025 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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clzAoKEh1vFGjRkbJ59misOgXtp+fn8b2ouItNTUVwP/NM32Ws7Oz1jkEBASU2afoF5Uhr3vUqFHo168frKysULVqVQQHB6s+Hr98+TIAqH3cXKRBgwY4fPgwAEAmk+Hzzz/Hu+++i2rVqqFVq1Z46aWXMGTIEHh7e+ud29MuX74MX19fODk5qbU//fV/2rNfQ+DJL1VtCm1tzr18+TJat25drF+dOnXKjP/o0SPVHNIi2rxPLVq0QLNmzVT7AwcORJMmTTB27Fi89NJLsLW1RWpqKrKyskr8SPvZueilfZ89+z4UFTdF70NAQADi4+MxZ84crFq1Cu3bt0evXr0wePBgtT8ENbl9+zYePHiAb7/9Ft9++61WuT7Lzs6u2B97z36dbt++rTZX1NHREY6OjoiKioKbmxtWr16tmrO7Zs0ahIaGqqaPAMDWrVvxySefICkpSe3msJL+UBw5ciQuXryII0eOlDmFBQCkUilq1KhRrP3KlSuYMmUKtmzZUux79tnvHW3eh2fl5+cXm9/v6emp+nkrnpl3ri8hhMXcTEmVAwtZMommTZuiQYMGWLNmDf73v/9hzZo1EEIYbXRLIpFo/MFc0k0hJY2ilNReFLtoDuuPP/6osTDR5o7hIk+PyJSnunXrarypQ1dxcXHo2bMnNm3ahJ07d2Ly5MlITEzE3r17DZ5rp4+yvlblda421q1bh2HDhhkc28rKChEREZg/fz5SU1MRHBwMpVIJLy8vrFq1SuM5zxY8pX2fafM+zJ49G0OHDsXmzZvx+++/Y/z48UhMTMSxY8c0FmhFiv6vFK2YoUnjxo1LPL+0/J7WvHlztT9ypk6dimnTpsHGxgbR0dH47rvvkJmZiStXriA1NRVffPGFqu+hQ4fQq1cvdOjQAV9//TV8fHxgY2ODZcuWaVxpZf78+VizZg1WrlypNlJfGplMBisr9Q9DCwsL8eKLL+LevXt4//330aBBA1SpUgXXr1/H0KFDi82V12c1hyNHjiAiIkKtLT09HbVq1YK7u7vRPlm5f/8+PDw8jBKLSBssZMlkBg0ahMmTJ+PUqVNYvXo16tatq7qrHXhyt+y5c+eKnXf27FnV8ZK4urpq/Jjt2VE8QxV9lO3l5VVmcWiMUYrAwEAAwOnTpw2OpUnRe3ru3Llio8znzp0r9p7Xrl0b7777Lt59912kpqYiLCwMs2fPxsqVKzXG1+U98Pf3x+7du/Hw4UO1UVltvv7lwd/fv9gd/AA0tj0rMjISu3btMkoeCoUCAJCdnQ3gyddg9+7daNu2bYX9MRQSEoKQkBB8+OGHOHLkCNq2bYvFixfjk08+AaD56+zp6QknJycUFhYa5Q+pkqxatUrtTv+i/zPAk585ixcvxrp165Ceng6JRIKBAweqjq9fvx52dnbYuXOn2k18y5YtK3adQ4cOYcKECYiLizP4D/Dk5GScP38eK1aswJAhQ1TtxvqeAYDQ0NBi8Yr++G7QoAFWrVqFrKysMkfWy5Keno7Q0FCDYhDpgnNkyWSKfvhPmTIFSUlJxX4ZdO/eHX/99ReOHj2qasvJycG3336LWrVqoWHDhiXGrl27Ns6ePYvbt2+r2v7991/88ccfRn0NkZGRcHZ2xmeffYaCgoJix5++fpUqVQAADx480Pt6np6e6NChA5YuXYorV66oHTPG6GGzZs3g5eWFxYsXq32sun37dqSkpKju2s7NzcXjx4/Vzq1duzacnJw0rtVZRJf3oHv37igsLMRXX32l1j537lxIJBJERUVp+7KMIjIyEkePHlV7Ktm9e/dKHAl9mo+PD7p06aK26aOgoAC///47bG1tVVMsoqOjUVhYiI8//rhYf4VCYdD327PkcrmqkC4SEhICKysrta97lSpVil3X2toar7zyCtavX6/xD7Gn/68Yom3btmrv89OFbNu2bVGrVi2sXLkS69atQ8eOHdVGka2trSGRSNQ+ubl06VKxp5TdvHkT0dHRaNeuHWbOnGlwzpo+3hdCYP78+QbHLuLq6lrse7BoHnzr1q0hhMCJEycMukZWVhYuXrxY4kokROWBI7JkMgEBAWjTpo1qzcFnC9lJkyZhzZo1iIqKwvjx4+Hm5oYVK1YgPT0d69evL/bx3NOGDx+OOXPmIDIyEiNGjMCtW7ewePFiBAcHQy6XG+01ODs7Y9GiRXj99dfxwgsvYMCAAfD09MSVK1fw22+/oW3btqpCrGnTpgCA8ePHIzIyUnXTm66+/PJLtGvXDi+88AJGjRqFgIAAXLp0Cb/99pvBj361sbHB559/jmHDhqFjx44YOHCgavmtWrVqqZbUOX/+PDp37ozo6Gg0bNgQUqkUGzduRGZmZqmvqXbt2qhatSoWL14MJycnVKlSBS1bttQ4b7Nnz56IiIjABx98gEuXLiE0NBS///47Nm/ejLi4OLUbuyrCe++9h5UrV+LFF1/EuHHjVMtv1axZE/fu3SuXeYHbt29XjUDfunULq1evRmpqKiZNmqSaf92xY0e8+eabSExMRFJSErp27QobGxukpqbi559/xvz58/Hqq68aJZ+9e/di7Nix6NevH+rVqweFQoEff/xRVaQWadq0KXbv3o05c+bA19cXAQEBaNmyJWbMmIF9+/ahZcuWGDlyJBo2bIh79+7hn3/+we7duzWu0WxMEokEr732Gj777DMAwEcffaR2vEePHpgzZw66deuG1157Dbdu3cLChQtRp04dnDp1StVv/PjxuH37Nt577z2sXbtWLUbjxo3LnCLxrAYNGqB27dqYMGECrl+/DmdnZ6xfv77cbqR8Vrt27eDu7o7du3cX+yTm119/xb///gvgyR9Sp06dUo289+rVS+217t69G0II9O7du0LyJgLA5bfItBYuXCgAiBYtWmg8fvHiRfHqq6+KqlWrCjs7O9GiRQuxdetWtT4lPaJ25cqVIjAwUNja2oqwsDCxc+fOEpffenYZqaLliJ5dZqhoWaS///67WP/IyEjh4uIi7OzsRO3atcXQoUPF8ePHVX0UCoUYN26c8PT0FBKJRLUUV0k5lPbaTp8+LV5++WXV+1K/fn0xefJkje9hWa9Vk3Xr1okmTZoImUwm3NzcxKBBg8S1a9dUx+/cuSNiY2NFgwYNRJUqVYSLi4to2bKl+Omnn9TiaFoGbfPmzaJhw4ZCKpWqvbZnvzZCPFne7J133hG+vr7CxsZG1K1bV8ycObPYUmMARGxsbLHX4e/vr7ZcUEnLb/Xo0aPYuZpyP3nypGjfvr2QyWSiRo0aIjExUXz55ZcCgMjIyCgWQ1+alt+ys7MTYWFhYtGiRRqXWvv2229F06ZNhb29vXBychIhISHivffeEzdu3CjztZb0/f7s919aWpoYPny4qF27trCzsxNubm4iIiJC7N69W+28s2fPig4dOgh7e3sBQO1rkJmZKWJjY4Wfn5+wsbER3t7eonPnzuLbb78t8bpCPPn+qFKlSrHcp06dqtOjn8+cOaNayuz+/fvFji9ZskTUrVtXyGQy0aBBA7Fs2bJi1+jYsaPG5dHw1HJXurwGIYT477//RJcuXYSjo6Pw8PAQI0eOVC0hp+/78HQ+ZRk/fryoU6dOsfai5b40bc/+XOrfv79o166dVtcjMhaJEEa6m4GI6DkUFxeHb775BtnZ2UZ9lDBRRUpLS0ODBg2wfft21RKIusjIyEBAQADWrl3LEVmqUCxkiYi09OjRI7Ubqu7evYt69erhhRdeMOqNOUSm8NZbb+HChQt6fS9PmjQJe/fuxV9//VUOmRGVjIUsEZGWwsLCEB4ejqCgIGRmZmLJkiW4ceMG9uzZgw4dOpg6PSKi5w5v9iIi0lL37t3xyy+/4Ntvv4VEIsELL7yAJUuWsIglIjIRjsgSERERkUXiOrJEREREZJFYyBIRERGRRWIhS1SK5cuXQyKR4NKlS6ZOpVxdunQJEokEy5cvL7Pv0KFDUatWLbU2iUSCadOmlUtuREREJWEhS2arqIgs2uzs7ODr64vIyEh8+eWXePjwoalTpAqkVCrxww8/oGXLlnBzc4OTkxPq1auHIUOG4NixYxWez9mzZ/Hee+8hLCwMTk5O8PHxQY8ePXD8+HGN/deuXYsXXngBdnZ28PT0xIgRI3Dnzh29r79nzx4MHz4c9erVg4ODAwIDA/HGG2/g5s2bGvsfOXIE7dq1g4ODA7y9vTF+/HhkZ2er9fn7778xduxYBAcHo0qVKqhZsyaio6Nx/vz5UnMpKChAw4YNIZFIMGvWLK1fw5YtW1TvSc2aNTF16tRij8B99ufA01tGRobW1yqyatUqSCQSODo6Fjv2119/YcyYMWjatClsbGx0flrb/v37S8z12e/Rzz77DK1atYKnpyfs7OxQt25dxMXFafWo3qI/PEt6r2fNmqX6A7y09+/p7ek/TpOSkjB48GD4+flBJpPBzc0NXbp0wbJly9Qe30tkDrhqAZm9jz76CAEBASgoKEBGRgb279+PuLg4zJkzB1u2bNH5cZC6eP311zFgwADIZLJyu4Y58Pf3x6NHj2BjY6PX+Y8ePYJUWr4/TsaPH4+FCxeid+/eGDRoEKRSKc6dO4ft27cjMDAQrVq1KtfrP+v777/HkiVL8Morr2DMmDHIysrCN998g1atWmHHjh3o0qWLqu+iRYswZswYdO7cGXPmzMG1a9cwf/58HD9+HH/++afqmfe6eP/993Hv3j3069cPdevWRVpaGr766its3boVSUlJ8Pb2VvVNSkpC586dERQUpLr+rFmzkJqaiu3bt6v6ff755/jjjz/Qr18/NG7cGBkZGfjqq6/wwgsv4NixY2jUqJHGXBYsWIArV67olP/27dvRp08fhIeHY8GCBUhOTsYnn3yCW7duYdGiRcX6F/0ceFrVqlV1umZ2djbee+89VKlSRePxbdu24fvvv0fjxo0RGBhYZgFfkvHjx6N58+ZqbXXq1FHbP3HiBMLCwjBgwAA4OTkhJSUF3333nepR0yXlqKsOHTrgxx9/VGt744030KJFC4waNUrVVlTYf//99xg9ejSqVauG119/HXXr1sXDhw+xZ88ejBgxAjdv3sT//vc/o+RGZBSme6gYUelKehysEELs2bNH2NvbC39/f5Gbm2uC7J5fmh4lW94yMjKERCIRI0eOLHZMqVSKzMzMCs1HCCGOHz8uHj58qNZ2584d4enpKdq2batqy8vLE1WrVhUdOnRQe7Tsr7/+KgCIL7/8Uq/rHzhwQBQWFhZrAyA++OADtfaoqCjh4+MjsrKyVG3fffedACB27typavvjjz9EXl6e2rnnz58XMplMDBo0SGMemZmZwsXFRXz00UdaPwJZCCEaNmwoQkNDRUFBgartgw8+EBKJRKSkpKjaSvs5oKv3339f1K9fXwwaNEjjY14zMjJUP09iY2N1evStECU/6ldbv/zyiwAg1qxZU2q/sh43PXPmzGKPYn5alSpV1B4bXOTo0aPC2tpatGvXTsjl8mLH//7772KPpSUyNU4tIIvUqVMnTJ48GZcvX8bKlSvVjp09exavvvoq3NzcYGdnh2bNmmHLli2q48ePH4dEIsGKFSuKxd25cyckEgm2bt0KoOQ5stu3b0fHjh3h5OQEZ2dnNG/eHKtXr1br8+eff6Jbt25wcXGBg4MDOnbsiD/++MMor3/o0KFwdHTE9evX0adPHzg6OsLT0xMTJkxQffRXUFAANzc3DBs2rNj5crkcdnZ2mDBhAoCS58hu2rQJjRo1gp2dHRo1aoSNGzdqzOfZObKXL1/GmDFjUL9+fdjb28Pd3R39+vXTe65xeno6hBBo27atxmt7eXmptT148ABxcXGqj0br1KmDzz//HEqlUtXn6Y9n586dC39/f9jb26Njx444ffp0mTk1bdq02MfT7u7uaN++PVJSUlRtp0+fxoMHD9C/f3+1j6pfeuklODo6Yu3atVq/D0/r0KEDrKysirW5ubmpXV8ul2PXrl0YPHgwnJ2dVe1DhgyBo6MjfvrpJ1VbmzZtYGtrqxazbt26CA4OVov5tEmTJqF+/foYPHiw1rn/999/+O+//zBq1Ci1kfwxY8ZACIFffvlF43kPHz4s9aPtmzdv4uzZsygoKCh2LDU1FXPnzsWcOXNK/PSgWrVqak9uK82VK1dw9uzZEo8/fPiw2DSJshR9vP/gwQOdzjOW6dOnQyKRYNWqVXBycip2vFmzZhg6dGjFJ0ZUChayZLFef/11AMDvv/+uajtz5gxatWqFlJQUTJo0CbNnz0aVKlXQp08fVRHWrFkzBAYGqv0CL7Ju3Tq4uroiMjKyxOsuX74cPXr0wL1795CQkIAZM2YgLCwMO3bsUPXZu3cvOnToALlcjqlTp+Kzzz7DgwcP0KlTJ7VHOBYUFODOnTtabU8XYQBQWFiIyMhIuLu7Y9asWejYsSNmz56Nb7/9FgBgY2ODl19+GZs2bUJ+fr7auZs2bUJeXh4GDBhQ4uv8/fff8corr0AikSAxMRF9+vTBsGHDSpwD+rS///4bR44cwYABA/Dll19i9OjR2LNnD8LDw5Gbm1vm+c/y9/cHAPz8889lnp+bm4uOHTti5cqVGDJkCL788ku0bdsWCQkJiI+PL9b/hx9+wJdffonY2FgkJCTg9OnT6NSpEzIzM3XOE3jyzHkPDw/Vfl5eHgBoLJDs7e1x8uTJYl9bfWVnZyM7O1vt+snJyVAoFGjWrJlaX1tbW4SFheHkyZOlxhRCIDMzUy1mkb/++gsrVqzAvHnzdJpPWnTNZ3Py9fVFjRo1NOYUEREBZ2dnODg4oFevXkhNTS3WJyEhAUFBQbh+/XqxY3FxcYiIiED37t21zrM0Q4YMQVBQkMZjw4YNg7OzM+zs7BAREVHi/xkhBO7cuYOMjAwcOnQI48ePh7W1NcLDw42Soy5yc3NVT6irWbNmhV+fSG+mHRAmKpk2Hym6uLiIJk2aqPY7d+4sQkJCxOPHj1VtSqVStGnTRtStW1fVlpCQIGxsbMS9e/dUbUUfAQ8fPrxYDkUf0T148EA4OTmJli1bikePHqnlUvSxsVKpFHXr1hWRkZFqHyXn5uaKgIAA8eKLL6raij6K1GZ7+mPCmJgYAUB89NFHajk0adJENG3aVLW/c+dOAUD8+uuvav26d+8uAgMDVftFH1U+/bFhWFiY8PHxEQ8ePFC1/f777wJAsakFAMTUqVPVXuuzjh49KgCIH374odgxbQwZMkQAEK6uruLll18Ws2bNUvsIusjHH38sqlSpIs6fP6/WPmnSJGFtbS2uXLkihPi/12xvby+uXbum6vfnn38KAOKdd97ROceDBw8KiUQiJk+erGq7ffu2kEgkYsSIEWp9z549q/ra3rlzR+drafLxxx8LAGLPnj2qtp9//lkAEAcPHizWv1+/fsLb27vUmD/++KMAIJYsWaLWrlQqRYsWLcTAgQOFEGV/3P20oo++i74WT2vevLlo1aqVan/dunVi6NChYsWKFWLjxo3iww8/FA4ODsLDw6PY+UX/L579SH3r1q1CKpWKM2fOqPppmlrwtLKmFnTs2LHY8T/++EO88sorYsmSJWLz5s0iMTFRuLu7Czs7O/HPP/8Ui3Hz5k21/+M1atQQ69atKzUvIcpnasG///4rAIi33367zOsTmROOyJJFc3R0VK1ecO/ePezduxfR0dF4+PChaiTz7t27iIyMRGpqqmqkpn///igoKMCGDRtUsX7//XfVR8Al2bVrFx4+fIhJkyYVu0GnaEQqKSkJqampeO2113D37l1VHjk5OejcuTMOHjyoGoELDQ3Frl27tNqevnmnyOjRo9X227dvj7S0NNV+p06d4OHhgXXr1qna7t+/j127dpX6Om/evImkpCTExMTAxcVF1f7iiy+iYcOGJZ5X5OnRx4KCAty9exd16tRB1apV8c8//5R5vibLli3DV199hYCAAGzcuBETJkxAUFAQOnfurDYC9/PPP6N9+/ZwdXVVG9Hu0qULCgsLcfDgQbW4ffr0QfXq1VX7LVq0QMuWLbFt2zad8rt16xZee+01BAQE4L333lO1e3h4IDo6GitWrMDs2bORlpaGQ4cOoX///qqb6x49eqTPW6Lm4MGDmD59OqKjo9GpUydVe1FsTTcs2tnZlXrts2fPIjY2Fq1bt0ZMTIzaseXLlyM5ORmff/65zrnqklN0dDSWLVuGIUOGoE+fPvj444+xc+dO3L17F59++mmxnIQQanfg5+fn45133sHo0aO1+t7V1v79+yGeeTBmmzZt8Msvv2D48OHo1asXJk2ahGPHjkEikSAhIaFYDDc3N+zatQu//vorPvroI3h4eBRbSaKiyOVyANA4pYDInHHVArJo2dnZqvmRFy5cgBACkydPxuTJkzX2v3XrFqpXr47Q0FA0aNAA69atw4gRIwA8mVbg4eGhVgQ86+LFiwBQ4t3bAFQfeT77i/9pWVlZcHV1haurq9rd7booWsbpaa6urrh//75qXyqV4pVXXsHq1auRl5cHmUyGDRs2oKCgoNRC9vLlywCezI98Vv369cssRh89eoTExEQsW7YM169fV/uFn5WVpdXre5aVlRViY2MRGxuLu3fv4o8//sDixYuxfft2DBgwAIcOHQLw5P0/depUsfemyK1bt9T2Nb3GevXqaZx6UpKcnBy89NJLePjwIQ4fPlxs7uw333yDR48eYcKECap5yYMHD0bt2rWxYcMGjUtB6eLs2bN4+eWX0ahRI3z//fdqx4r+qCia4vC0x48flzgnNCMjAz169ICLiwt++eUXWFtbq47J5XIkJCRg4sSJ8PPz0zlffXMq0q5dO7Rs2RK7d+8u81pz587FnTt3MH36dJ3zNIY6deqgd+/e2LBhAwoLC9XeR1tbW9X//5deegmdO3dG27Zt4eXlhZdeesnga+sy3aNo/jSXNSRLw0KWLNa1a9eQlZWlWtamaJRzwoQJJc5xfXoJnP79++PTTz/FnTt34OTkhC1btmDgwIEGLyNVlMfMmTMRFhamsU9R4ZKfn4979+5pFdfT01Ptl+DT/y7NgAED8M0336iWO/rpp5/QoEEDhIaGanW+PsaNG4dly5YhLi4OrVu3houLCyQSCQYMGGCU+aDu7u7o1asXevXqhfDwcBw4cACXL1+Gv78/lEolXnzxRbVR0afVq1fP4Os/LT8/H3379sWpU6ewc+dOjX/kuLi4YPPmzbhy5QouXboEf39/+Pv7o02bNvD09NR5GamnXb16FV27doWLiwu2bdtWbETNx8cHADSuL3vz5k34+voWa8/KykJUVBQePHiAQ4cOFesza9Ys5Ofno3///qob+K5duwbgyYj/pUuX4OvrW+zGMU05PVsI37x5Ey1atCjzdfv5+eHcuXOl9snKysInn3yCMWPGQC6Xq0Yds7OzIYTApUuX4ODgUOxmQWPz8/NDfn4+cnJy1G64e1abNm3g4+ODVatWlVrIFn0aVNJoetE8cl2WdatTpw6kUimSk5O1PofIHLCQJYtVtDZiUdEaGBgI4MlNTtqMcvbv3x/Tp0/H+vXrUa1aNcjl8lJvfgKA2rVrA3hyJ/qz60I+28fZ2bnMPI4cOYKIiIgycwWe3Ln/7BO1tNGhQwf4+Phg3bp1aNeuHfbu3YsPPvig1HOKbq7SdENNWcUDAPzyyy+IiYnB7NmzVW2PHz8ul7uxmzVrhgMHDuDmzZvw9/dH7dq1kZ2drfVIt6bXeP78ea3ea6VSiSFDhmDPnj346aef0LFjx1L716xZU3UjzYMHD3DixAm88sorWuWpyd27d9G1a1fk5eVhz549qgLxaY0aNYJUKsXx48cRHR2tas/Pz0dSUpJaG/Dk69SzZ0+cP38eu3fv1vhx/JUrV3D//n0EBwcXO/bZZ5/hs88+w8mTJ0v8Q66o/fjx42pF640bN3Dt2jW19U1LkpaWVuKoe5H79+8jOzsbX3zxBb744otixwMCAtC7d29s2rSpzOsZIi0tDXZ2dlqNvD9+/LjMTy08PT3h4OBQ4v/Fc+fOwcHBQeMNeiVxcHBAp06dsHfvXly9elWvkXYikzDpDF2iUmizjmxAQIDaTVfh4eHCzc1N3Lhxo9g5t27dKtYWEhIiIiIixIABA4SPj0+xdTmfvdkrKytLODk5iRYtWpR4s1dhYaGoXbu2qFu3brF1Rp/N4969e2LXrl1abU9fr6SbVaZOnarxBpVx48aJKlWqiDlz5ggA4r///lM7buybvdzc3MTQoUPV+nzxxRcCgMb1K8ty8+ZN1Y06T8vLyxNhYWHCyspK3L59WwghxLRp0wQAsWPHjmL979+/r1q3tKybveLi4srMa8yYMQKA+Oabb3R+TaNHjxZWVlbir7/+0vlcIYTIzs4WLVq0EE5OTuL48eOl9u3WrZvw8fFRWxv0+++/FwDE9u3bVW0KhUL06tVLSKVS8dtvv5UY78SJE2Ljxo1q2zfffCMAiKFDh4qNGzeqfd9o0qBBAxEaGioUCoWq7cMPPxQSiUTt+1PT/9vffvtNABDjx49Xa79x44ZISUkR+fn5QgghcnJyiuW5ceNGERERIezs7MTGjRvFsWPHNOZX1s1ely9fLnazoaZck5KShI2NjejVq5eqLTs7W+Tk5BTrW7SO7NM3C5akT58+wtnZWVy+fLlYXk5OTqJPnz4lnlvSOrJ//PGHsLa2Fh07dtT4s+v48eNi+fLlZeZGVJE4Iktmb/v27Th79iwUCgUyMzOxd+9e7Nq1C/7+/tiyZYvax2cLFy5Eu3btEBISgpEjRyIwMBCZmZk4evQorl27hn///Vctdv/+/TFlyhTY2dlhxIgRxdblfJazszPmzp2LN954A82bN8drr70GV1dX/Pvvv8jNzcWKFStgZWWF77//HlFRUQgODsawYcNQvXp1XL9+Hfv27YOzszN+/fVXADBojqwu+vfvjwULFmDq1KkICQkpcdmgpyUmJqJHjx5o164dhg8fjnv37mHBggUIDg4u84aUl156CT/++CNcXFzQsGFDHD16FLt374a7u7te+V+7dg0tWrRAp06d0LlzZ3h7e+PWrVtYs2YN/v33X8TFxalGnyZOnIgtW7bgpZdewtChQ9G0aVPk5OQgOTkZv/zyCy5duqQ2UlWnTh20a9cOb731FvLy8jBv3jy4u7uXODWhyLx58/D111+jdevWcHBwKLae8csvv6x6OtOMGTNw+vRptGzZElKpFJs2bcLvv/+OTz75pNgToIqmSohnbiR61qBBg/DXX39h+PDhSElJUVvn1dHREX369FHtf/rpp2jTpg06duyIUaNG4dq1a5g9eza6du2Kbt26qfq9++672LJlC3r27Il79+4Ve01Fa8W+8MILeOGFF9SOFU0xCA4OVrt2SWbOnIlevXqha9euGDBgAE6fPo2vvvoKb7zxhtr3Z5s2bdCkSRM0a9YMLi4u+Oeff7B06VL4+fkVe8JUQkICVqxYofr0wsHBQWMumzZtwl9//VXs2OXLl1Wf9BQtmfXJJ58AePIpRdGSf8CT5bee/Tr1798f9vb2aNOmDby8vPDff//h22+/hYODA2bMmKHql5qaii5duqB///5o0KABrKyscPz4caxcuRK1atXC22+/Xeb7V/SI2xdeeAGjRo1CrVq1cOnSJXz77beQSCT47LPPyozxrDZt2mDhwoUYM2YMGjRooPZkr/3792PLli2q94PIbJi6kiYqSdFoaNFma2srvL29xYsvvijmz5+v8ckzQghx8eJFMWTIEOHt7S1sbGxE9erVxUsvvSR++eWXYn1TU1NV8Q8fPlxiDs8uY7NlyxbRpk0bYW9vL5ydnUWLFi2KPY3n5MmTom/fvsLd3V3IZDLh7+8voqOj1ZZG0peuI7JKpVL4+fkJAOKTTz4pdlzTiKwQQqxfv14EBQUJmUwmGjZsKDZs2KDxyV54ZkT2/v37YtiwYcLDw0M4OjqKyMhIcfbsWeHv76/XiKxcLhfz588XkZGRokaNGsLGxkY4OTmJ1q1bi++++05tmTMhhHj48KFISEgQderUEba2tsLDw0O0adNGzJo1SzVa9/QSRrNnzxZ+fn5CJpOJ9u3bi3///bfMnIqWeippe/p7ZuvWrarRUwcHB9GqVSvx008/aYzbtGnTMpfEEkIIf3//Eq+t6clrhw4dEm3atBF2dnbC09NTxMbGFvs/VLSkVElbaXRZfqvIxo0bRVhYmJDJZKJGjRriww8/VH19inzwwQciLCxMuLi4CBsbG1GzZk3x1ltviYyMjGLxSlp+S1M/Tf9/SlsOr2PHjmp9NS2/NX/+fNGiRQvh5uYmpFKp8PHxEYMHDxapqalq/W7fvi1GjRolGjRoIKpUqSJsbW1F3bp1RVxcnOqTBW2kpKSI/v37Cy8vLyGVSoWXl5cYMGCAxmXpnlbSiGyREydOiNdee034+voKGxsb4erqKjp37ixWrFhR7FMrIlOTCFHGn/1ERJXQpUuXEBAQgJkzZ6pWEjC1hw8fws3NDfPmzUNsbKyp0yEiMntcR5aIyEwcPHgQ1atXx8iRI02dChGRRWAhS0RkJnr06IFLly6VuGwVERGpYyFLRERERBaJc2SJiIiIyCJxRJaIiIiILBILWSIiIiKySCxkiYiIiMgisZAlIiIiIovEQpaIiIiILBILWSIiIiKySFJTJ1DelEolbty4AScnJ0gkElOnQ0RERGZMCIGHDx/C19cXVlYc7zN3lb6QvXHjBvz8/EydBhEREVmQq1evokaNGqZOg8pQ6QtZJycnAE++IZ2dnU2cDRFVJvO/Xo6DFz3KLX6H2nfw9pih5RafiIqTy+Xw8/NT1Q9k3ip9IVs0ncDZ2ZmFLBEZlZ2dPWxsHco1Pn9uEZkGpyNaBk7+ICIiIiKLxEKWiIiIiCwSC1kiIiIiskgsZImIiIjIIrGQJSIiIiKLxEKWiIiIiCwSC1kiIiIiskgsZImIiIjIIrGQJSIiIiKLxEKWiIiIiCwSC1kiIiIiskhSUydARGRJlq5Yg7v3swEAWXI5wjweGDW+1FoCJ2cXAIC7q6NRYxMRVTYsZImIdHD3fjb2pXr+/z3PUvvqI6LubUyMG2n0uERElRGnFhARERGRRWIhS0REREQWiYUsEREREVkkzpHV0pkzZ6BQKHQ+TyqVIjg4uFLH1zc24xseGwBSUlL0jh8UFKTzeUREROaChayWFAoFAgMDdT4vLS2t0sfXNzbjGx67KH5ISIjO8ZOTk3U+h4iIyJxwagERERERWSQWskRERERkkVjIEhEREZFFMmkhm5iYiObNm8PJyQleXl7o06cPzp07p9bnzTffRO3atWFvbw9PT0/07t0bZ8+eNVHGRERERGQuTFrIHjhwALGxsTh27Bh27dqFgoICdO3aFTk5Oao+TZs2xbJly5CSkoKdO3dCCIGuXbuisLDQhJkTERERkamZdNWCHTt2qO0vX74cXl5eOHHiBDp06AAAGDVqlOp4rVq18MknnyA0NBSXLl1C7dq1KzRfIiIiIjIfZrX8VlZWFgDAzc1N4/GcnBwsW7YMAQEB8PPz09gnLy8PeXl5qn25XG78RImIiIjI5MzmZi+lUom4uDi0bdsWjRo1Ujv29ddfw9HREY6Ojti+fTt27doFW1tbjXESExPh4uKi2koqeImIiIjIsplNIRsbG4vTp09j7dq1xY4NGjQIJ0+exIEDB1CvXj1ER0fj8ePHGuMkJCQgKytLtV29erW8UyciIiIiEzCLqQVjx47F1q1bcfDgQdSoUaPY8aLR1bp166JVq1ZwdXXFxo0bMXDgwGJ9ZTIZZDJZRaRNRERERCZk0kJWCIFx48Zh48aN2L9/PwICArQ6RwihNg+WiIiIiJ4/Ji1kY2NjsXr1amzevBlOTk7IyMgA8GQE1t7eHmlpaVi3bh26du0KT09PXLt2DTNmzIC9vT26d+9uytSJiIiIyMRMOkd20aJFyMrKQnh4OHx8fFTbunXrAAB2dnY4dOgQunfvjjp16qB///5wcnLCkSNH4OXlZcrUiYiIiMjETD61oDS+vr7Ytm1bBWVDRERERJbEbFYtICIiIiLShVmsWmAJpFIp0tLS9DqvssfXNzbjGx67qF9ycnK5xSciIjJX/E2mpeDgYMY3QWzGL1tQUFC5xiciIjJXnFpARERERBaJhSwRERERWSQWskRERERkkVjIEhEREZFF4s1eRERlWLpiDe7ezwYAZMnlCPN4YNT4UmsJnJxdAADuro5GjU1EVJmxkCUiKsPd+9nYl+r5//c8S+2rj4i6tzExbqTR4xIRVXacWkBEREREFomFLBERERFZJBayRERERGSROEdWS2fOnIFCodD5PKlUqtWTnSw5vr6xGd/w2ACQkpKid3w+FYyIiCwZC1ktKRQKBAYG6nxeWlpapY+vb2zGNzx2UfyQkBCd4ycnJ+t8DhERkTnh1AIiIiIiskgsZImIiIjIIrGQJSIiIiKLZNJCNjExEc2bN4eTkxO8vLzQp08fnDt3TnX83r17GDduHOrXrw97e3vUrFkT48ePR1ZWlgmzJiIiIiJzYNJC9sCBA4iNjcWxY8ewa9cuFBQUoGvXrsjJyQEA3LhxAzdu3MCsWbNw+vRpLF++HDt27MCIESNMmTYRERERmQGTrlqwY8cOtf3ly5fDy8sLJ06cQIcOHdCoUSOsX79edbx27dr49NNPMXjwYCgUCkilXHSBiIiI6HllVpVg0ZQBNze3Uvs4OzuXWMTm5eUhLy9PtS+Xy42bJBERERGZBbO52UupVCIuLg5t27ZFo0aNNPa5c+cOPv74Y4waNarEOImJiXBxcVFtfn5+5ZUyEREREZmQ2RSysbGxOH36NNauXavxuFwuR48ePdCwYUNMmzatxDgJCQnIyspSbVevXi2njImIiIjIlMxiasHYsWOxdetWHDx4EDVq1Ch2/OHDh+jWrRucnJywceNG2NjYlBhLJpNBJpOVZ7pEREREZAZMOiIrhMDYsWOxceNG7N27FwEBAcX6yOVydO3aFba2ttiyZQvs7OxMkCkRERERmRuTjsjGxsZi9erV2Lx5M5ycnJCRkQEAcHFxgb29vaqIzc3NxcqVKyGXy1U3b3l6esLa2tqU6RMRERGRCZm0kF20aBEAIDw8XK192bJlGDp0KP755x/8+eefAIA6deqo9UlPT0etWrUqIk0iIiIiMkMmLWSFEKUeDw8PL7MPERERET2fzGbVAiIiIiIiXZjFqgWWQCqVIi0tTa/zKnt8fWMzvuGxi/olJyeXW3wiIiJzxd9kWgoODmZ8E8Rm/LIFBQWVa3wiIiJzxakFRERERGSRWMgSERERkUViIUtEREREFomFLBERERFZJN7sRUSkwdIVa3D3fjYAIEsuR5jHA6PFllpL4OTsotp3d3U0WmwioucJC1kiIg3u3s/GvlTP/7/nWWpfXUXUvY2JcSONGpOI6HnEqQVEREREZJFYyBIRERGRRWIhS0REREQWiXNktXTmzBkoFAqdz5NKpVo92cmS4+sbm/ENjw0AKSkpesfnU8GIiMiSsZDVkkKhQGBgoM7npaWlVfr4+sZmfMNjF8UPCQnROX5ycrLO5xAREZkTTi0gIiIiIovEQpaIiIiILBILWSIiIiKySCYtZBMTE9G8eXM4OTnBy8sLffr0wblz59T6fPvttwgPD4ezszMkEgkePHhgmmSJiIiIyKyYtJA9cOAAYmNjcezYMezatQsFBQXo2rUrcnJyVH1yc3PRrVs3/O9//zNhpkRERERkbky6asGOHTvU9pcvXw4vLy+cOHECHTp0AADExcUBAPbv31/B2RERERGROTOr5beysrIAAG5ubnrHyMvLQ15enmpfLpcbnBcRERERmR+zudlLqVQiLi4Obdu2RaNGjfSOk5iYCBcXF9Xm5+dnxCyJiIiIyFyYTSEbGxuL06dPY+3atQbFSUhIQFZWlmq7evWqkTIkIiIiInNiFlMLxo4di61bt+LgwYOoUaOGQbFkMhlkMpmRMiMiIiIic2XSQlYIgXHjxmHjxo3Yv38/AgICTJkOEREREVkQkxaysbGxWL16NTZv3gwnJydkZGQAAFxcXGBvbw8AyMjIQEZGBi5cuADgyfPhnZycULNmTYNuCiMiIiIiy2bSObKLFi1CVlYWwsPD4ePjo9rWrVun6rN48WI0adIEI0eOBAB06NABTZo0wZYtW0yVNhERERGZAZNPLSjLtGnTMG3atPJPhoiIiIgsitmsWkBEREREpAuzWLXAEkilUqSlpel1XmWPr29sxjc8dlG/5OTkcotPRERkrvibTEvBwcGMb4LYjF+2oKCgco1PRERkrji1gIiIiIgsEgtZIiIiIrJILGSJiIiIyCKxkCUiIiIii8SbvYiIACxdsQZ372er9rPkcoR5PDBafKm1BE7OLgAAd1dHo8UlInqesZAlIgJw93429qV6PtXiWWJffUTUvY2JcSONGpOI6HnHqQVEREREZJFYyBIRERGRRWIhS0REREQWiXNktXTmzBkoFAqdz5NKpVo92cmS4+sbm/ENjw0AKSkpesfnU8GIiMiSsZDVkkKhQGBgoM7npaWlVfr4+sZmfMNjF8UPCQnROX5ycrLO5xAREZkTTi0gIiIiIovEQpaIiIiILBILWSIiIiKySCYtZBMTE9G8eXM4OTnBy8sLffr0wblz59T6PH78GLGxsXB3d4ejoyNeeeUVZGZmmihjIiIiIjIXJi1kDxw4gNjYWBw7dgy7du1CQUEBunbtipycHFWfd955B7/++it+/vlnHDhwADdu3EDfvn1NmDURERERmQOTrlqwY8cOtf3ly5fDy8sLJ06cQIcOHZCVlYUlS5Zg9erV6NSpEwBg2bJlCAoKwrFjx9CqVStTpE1EREREZsCs5shmZWUBANzc3AAAJ06cQEFBAbp06aLq06BBA9SsWRNHjx7VGCMvLw9yuVxtIyIiIqLKx2wKWaVSibi4OLRt2xaNGjUCAGRkZMDW1hZVq1ZV61utWjVkZGRojJOYmAgXFxfV5ufnV96pExEREZEJmE0hGxsbi9OnT2Pt2rUGxUlISEBWVpZqu3r1qpEyJCIiIiJzYhZP9ho7diy2bt2KgwcPokaNGqp2b29v5Ofn48GDB2qjspmZmfD29tYYSyaTQSaTlXfKRERERGRiJh2RFUJg7Nix2LhxI/bu3YuAgAC1402bNoWNjQ327Nmjajt37hyuXLmC1q1bV3S6RERERGRGTDoiGxsbi9WrV2Pz5s1wcnJSzXt1cXGBvb09XFxcMGLECMTHx8PNzQ3Ozs4YN24cWrduzRULiIiIiJ5zJi1kFy1aBAAIDw9Xa1+2bBmGDh0KAJg7dy6srKzwyiuvIC8vD5GRkfj6668rOFMiIiIiMjcmLWSFEGX2sbOzw8KFC7Fw4cIKyIiIiIiILIXZrFpARERERKQLs1i1wBJIpVKkpaXpdV5lj69vbMY3PHZRv+Tk5HKLT0REZK74m0xLwcHBjG+C2IxftqCgoHKNT0REZK44tYCIiIiILBILWSIiIiKySCxkiYiIiMgisZAlIiIiIovEm72I6Lm1dMUa3L2fDQDIkssR5vHAqPGl1hI4ObsAANxdHY0am4iIWMgS0XPs7v1s7Ev1/P97nqX21UdE3duYGDfS6HGJiOgJTi0gIiIiIovEQpaIiIiILBILWSIiIqLnzMGDB9GzZ0/4+vpCIpFg06ZNZZ6zf/9+vPDCC5DJZKhTpw6WL19e7nmWhXNktXTmzBkoFAqdz5NKpVo92cmS4+sbm/ENjw0AKSkpesfnU8GIiJ5POTk5CA0NxfDhw9G3b98y+6enp6NHjx4YPXo0Vq1ahT179uCNN96Aj48PIiMjKyBjzVjIakmhUCAwMFDn89LS0ip9fH1jM77hsYvih4SE6Bw/OTlZ53OIiKhyiIqKQlRUlNb9Fy9ejICAAMyePRvAk8ejHz58GHPnzmUhS0RERPS8efz4MfLz840WTwgBiUSi1iaTySCTyQyOffToUXTp0kWtLTIyEnFxcQbHNgQLWSIiIqIK9vjxY7i6euPx4yyjxXR0dER2drZa29SpUzFt2jSDY2dkZKBatWpqbdWqVYNcLsejR49gb29v8DX0wUKWiIiIqILl5+fj8eMsvBw1HzY2hheBBQWPsHH727h69SqcnZ1V7cYYjTVnJl21oKw75jIzMzF06FD4+vrCwcEB3bp1Q2pqqmmSJSIiIjIyGxt72Ng4GGF7Ugw7OzurbcYqZL29vZGZmanWlpmZCWdnZ5ONxgImLmSL7phbuHBhsWNCCPTp0wdpaWnYvHkzTp48CX9/f3Tp0gU5OTkmyJaIiIjo+dS6dWvs2bNHrW3Xrl1o3bq1iTJ6wqRTC0q7Yy41NRXHjh3D6dOnVUsQLVq0CN7e3lizZg3eeOONikyViIiIyOjyZdYQNtYGxymw0i1GdnY2Lly4oNpPT09HUlIS3NzcULNmTSQkJOD69ev44YcfAACjR4/GV199hffeew/Dhw/H3r178dNPP+G3334zOHdDmO0DEfLy8gAAdnZ2qjYrKyvIZDIcPny41PPkcrnaRkRERET/5/jx42jSpAmaNGkCAIiPj0eTJk0wZcoUAMDNmzdx5coVVf+AgAD89ttv2LVrF0JDQzF79mx8//33Jl16CzDjm70aNGig+ovgm2++QZUqVTB37lxcu3YNN2/eLPG8xMRETJ8+vQIzJSIiIrIs4eHhEEKUeFzTU7vCw8Nx8uTJcsxKd2Y7ImtjY4MNGzbg/PnzcHNzg4ODA/bt24eoqChYWZWcdkJCArKyslTb1atXKzBrIiIiIqooZjsiCwBNmzZFUlISsrKykJ+fD09PT7Rs2RLNmjUr8RxjLfxLREREVN4KpVawsjF8XLFQmO3YZLmyiFft4uICT09PpKam4vjx4+jdu7epUyIiIiIiEzPpiGxZd8z9/PPP8PT0RM2aNZGcnIy3334bffr0QdeuXU2YNRERERGZA5MWssePH0dERIRqPz4+HgAQExOD5cuX4+bNm4iPj0dmZiZ8fHwwZMgQTJ482VTpEhEREZEZMWkhW9Ydc+PHj8f48eMrMCMiIiKiilNgKwVsDS/HCsz7tqdyYxFzZImIiIiInvV8lu96kEqlSEtL0+u8yh5f39iMb3json7JycnlFp+IiMhc8TeZlooek8v4FRub8csWFBRUrvGJiIjMFQtZIiIiIhNR2FhBYoR1ZBVcR5aIiIiIyHKwkCUiIiIii8RCloiIiIgsEufIEtFzY+mKNbh7P1u1nyWXI8zjgdHiS60lcHJ2Ue27uzoaLTYRVU4KW2tAZm14HBgewxKxkCWi58bd+9nYl+r5VItniX31EVH3NibGjTRqTCIiKhmnFhARERGRRWIhS0REREQWiVMLiIiIiEykUGoFidTwccXCwudzbJKFrJbOnDkDhUKh83lSqVSrJztZcnx9YzO+4bEBICUlRe/4fCoYERFZMhayWlIoFAgMDNT5vLS0tEofX9/YjG947KL4ISEhOsdPTk7W+RwiIiJz8nyOQxMRERGRxeOILBEREZGJ5MusoDTKOrLP59jk8/mqiYiIiMjimbSQPXjwIHr27AlfX19IJBJs2rRJ7Xh2djbGjh2LGjVqwN7eHg0bNsTixYtNkywRERERmRWTFrI5OTkIDQ3FwoULNR6Pj4/Hjh07sHLlSqSkpCAuLg5jx47Fli1bKjhTIiIiIjI3Jp0jGxUVhaioqBKPHzlyBDExMQgPDwcAjBo1Ct988w3++usv9OrVq4KyJCIiIiofChtrCFvD58gWKg2PYYnMeo5smzZtsGXLFly/fh1CCOzbtw/nz59H165dSzwnLy8PcrlcbSMiIiKiysesC9kFCxagYcOGqFGjBmxtbdGtWzcsXLgQHTp0KPGcxMREuLi4qDY/P78KzJiIiIiIKorZF7LHjh3Dli1bcOLECcyePRuxsbHYvXt3ieckJCQgKytLtV29erUCMyYiIiKiimK268g+evQI//vf/7Bx40b06NEDANC4cWMkJSVh1qxZ6NKli8bzZDIZZDJZRaZKREREpJdCGyvAxvBxxcJCsx6bLDdm+6oLCgpQUFAAKyv1FK2traFUKk2UFRERERGZC5OOyGZnZ+PChQuq/fT0dCQlJcHNzQ01a9ZEx44dMXHiRNjb28Pf3x8HDhzADz/8gDlz5pgwayIiIiIyByYtZI8fP46IiAjVfnx8PAAgJiYGy5cvx9q1a5GQkIBBgwbh3r178Pf3x6efforRo0ebKmUiIiIiMhMmLWTDw8MhhCjxuLe3N5YtW1aBGRERERFVHGuZgFRWci2ktVLqqcrMbOfIEhERERGVxmxXLTA3UqkUaWlpep1X2ePrG5vxDY9d1C85Obnc4hMREZkr/ibTUnBwMOObIDbjly0oKKhc4xMREZkrFrJEREREJiK1VkIqNcKyotbP59KknCNLRERERBaJhSwRERERWSQWskRERERkkThHlogqraUr1uDu/WzVfpZcjjCPB0aLL7WWwMnZRbXv7upotNhE9HywlSkhlRUaHMdK+XzOkWUhS0SV1t372diX6vlUi2eJffURUfc2JsaNNGpMIiLSHqcWEBEREZFFYiFLRERERBaJUwuIiIiITERqo4TUxgjzWxWcI0ulOHPmDBQKhc7nSaVSrZ7sZMnx9Y3N+IbHBoCUlBS94/OpYEREZMlYyGpJoVAgMDBQ5/PS0tIqfXx9YzO+4bGL4oeEhOgcPzk5WedziIiIzAnnyBIRERGRRWIhS0REREQWiVMLiIiIiEzExqYQNraGPxBBojA8hiUy6YjswYMH0bNnT/j6+kIikWDTpk1qxyUSicZt5syZpkmYiIiIiMyGSQvZnJwchIaGYuHChRqP37x5U21bunQpJBIJXnnllQrOlIiIiIjMjUmnFkRFRSEqKqrE497e3mr7mzdvRkREhN53mBMRERFR5WExc2QzMzPx22+/YcWKFaX2y8vLQ15enmpfLpeXd2pEREREerGxUcLGGA9EMEYMC2QxqxasWLECTk5O6Nu3b6n9EhMT4eLiotr8/PwqKEMiIiIiqkgWU8guXboUgwYNgp2dXan9EhISkJWVpdquXr1aQRkSERERUUWyiKkFhw4dwrlz57Bu3boy+8pkMshksgrIioiIiIhMySIK2SVLlqBp06YIDQ01dSpERERERmMrU8JGZvj8Vknh8zlH1qSFbHZ2Ni5cuKDaT09PR1JSEtzc3FCzZk0AT27W+vnnnzF79mxTpUlEREREZsikhezx48cRERGh2o+PjwcAxMTEYPny5QCAtWvXQgiBgQMHmiJFIiIiIjJTJi1kw8PDIYQotc+oUaMwatSoCsqIiIiIiCyFRcyRJSIiIqqMbGwLYSsrNDyQwggxLBALWS1JpVKkpaXpdV5lj69vbMY3PHZRv+Tk5HKLT0REZK74m0xLwcHBjG+C2IxftqCgoHKNT0REZK4s5oEIRERERERP44gsERERkYlIpUpIpYavAas0QgxLxBFZIiIiIrJILGSJiIiIyCJxagERVRpLV6zB3fvZqv0suRxhHg+MFl9qLYGTs4tq393V0WixiYhIdyxkiajSuHs/G/tSPZ9q8Syxrz4i6t7GxLiRRo1JRM83W5lp15FduHAhZs6ciYyMDISGhmLBggVo0aJFif3nzZuHRYsW4cqVK/Dw8MCrr76KxMRE2NnZ6Zu5QTi1gIiIiOg5tG7dOsTHx2Pq1Kn4559/EBoaisjISNy6dUtj/9WrV2PSpEmYOnUqUlJSsGTJEqxbtw7/+9//Kjjz/8NCloiIiOg5NGfOHIwcORLDhg1Dw4YNsXjxYjg4OGDp0qUa+x85cgRt27bFa6+9hlq1aqFr164YOHAg/vrrrwrO/P+wkCUiIiKqJORyudqWl5ensV9+fj5OnDiBLl26qNqsrKzQpUsXHD16VOM5bdq0wYkTJ1SFa1paGrZt24bu3bsb/4VoiXNktXTmzBkoFAqdz5NKpVo92cmS4+sbm/ENjw0AKSkpesfnU8GIiEzLxurJZijx/2P4+fmptU+dOhXTpk0r1v/OnTsoLCxEtWrV1NqrVauGs2fParzGa6+9hjt37qBdu3YQQkChUGD06NEmnVrAQlZLCoUCgYGBOp+XlpZW6ePrG5vxDY9dFD8kJETn+MnJyTqfQ0RE5u3q1atwdnZW7ctkMqPF3r9/Pz777DN8/fXXaNmyJS5cuIC3334bH3/8MSZPnmy06+iChSwRERFRJeHs7KxWyJbEw8MD1tbWyMzMVGvPzMyEt7e3xnMmT56M119/HW+88QYAICQkBDk5ORg1ahQ++OADWFlV/IxVzpElIiIies7Y2tqiadOm2LNnj6pNqVRiz549aN26tcZzcnNzixWr1tbWAAAhRPklWwqOyBIRERGZiJ01ILM2PI6VHjHi4+MRExODZs2aoUWLFpg3bx5ycnIwbNgwAMCQIUNQvXp1JCYmAgB69uyJOXPmoEmTJqqpBZMnT0bPnj1VBW1FM+mI7MGDB9GzZ0/4+vpCIpFg06ZNxfqkpKSgV69ecHFxQZUqVdC8eXNcuXKl4pMlIiIiqkT69++PWbNmYcqUKQgLC0NSUhJ27NihugHsypUruHnzpqr/hx9+iHfffRcffvghGjZsiBEjRiAyMhLffPONqV6CaUdkc3JyEBoaiuHDh6Nv377Fjl+8eBHt2rXDiBEjMH36dDg7O+PMmTMme3oEERERUWUyduxYjB07VuOx/fv3q+1LpVJMnToVU6dOrYDMtGPSQjYqKgpRUVElHv/ggw/QvXt3fPHFF6q22rVrV0RqRERERGTmzPZmL6VSid9++w316tVDZGQkvLy80LJlS43TD56Wl5dXbDFgIiIiInNkI/m/tWQN2iSmfiWmYbaF7K1bt5CdnY0ZM2agW7du+P333/Hyyy+jb9++OHDgQInnJSYmwsXFRbU9uzAwEREREVUOZlvIKpVKAEDv3r3xzjvvICwsDJMmTcJLL72ExYsXl3heQkICsrKyVNvVq1crKmUiIiIiqkBmu/yWh4cHpFIpGjZsqNYeFBSEw4cPl3ieTCYz6lMsiIiIiMg8mW0ha2tri+bNm+PcuXNq7efPn4e/v7+JsiIiIiIyHpkUsDNGNWa2FV35MunLzs7OxoULF1T76enpSEpKgpubG2rWrImJEyeif//+6NChAyIiIrBjxw78+uuvxZaDICIiIqLnj0kL2ePHjyMiIkK1Hx8fDwCIiYnB8uXL8fLLL2Px4sVITEzE+PHjUb9+faxfvx7t2rUzVcpEREREZCZMWsiGh4eX+Wze4cOHY/jw4RWUERERERFZiud0RgURERGR6RWtA2uoQrNdh6p8sZDVklQqRVpaml7nVfb4+sZmfMNjF/VLTk4ut/hERETmir/JtBQcHMz4JojN+GULCgoq1/hERETm6jkdiCYiIiIiS8cRWSIiIiITsbcWsLMu/cZ3bUiMEMMScUSWiIiIiCwSC1kiIiIiskicWkBEFmnpijW4ez9brS1LLkeYxwOjxJdaS+Dk7KLW5u7qaJTYRERkHCxkicgi3b2fjX2pns+0Pruvv4i6tzExbqTR4hERaSKzBuysDY8jjBDDEnFqARERERFZJBayRERERGSRWMgSERERkUXiHFktnTlzBgqFQufzpFKpVk92suT4+sZmfMNjA0BKSore8flUMCIi07KxerIZSvGcDk2ykNWSQqFAYGCgzuelpaVV+vj6xmZ8w2MXxQ8JCdE5fnJyss7nEBERmZPntH4nIiIiIkvHQpaIiIiILBKnFhARERGZiLHWkVVyHdmKd/DgQfTs2RO+vr6QSCTYtGmT2vGhQ4dCIpGobd26dTNNskRERERkVkxayObk5CA0NBQLFy4ssU+3bt1w8+ZN1bZmzZoKzJCIiIiIzJVJpxZERUUhKiqq1D4ymQze3t4VlBERERERWQqznyO7f/9+eHl5wdXVFZ06dcInn3wCd3f3Evvn5eUhLy9PtS+XyysiTSIiIiKd2UoAW2OsIysxPIYlMutVC7p164YffvgBe/bsweeff44DBw4gKioKhYWFJZ6TmJgIFxcX1ebn51eBGRMRERFRRTHrEdkBAwao/h0SEoLGjRujdu3a2L9/Pzp37qzxnISEBMTHx6v25XI5i1kiIiKiSsisR2SfFRgYCA8PD1y4cKHEPjKZDM7OzmobEREREVU+Zj0i+6xr167h7t278PHxMXUqRERERAaTWQMya2FwnMLndB1Zkxay2dnZaqOr6enpSEpKgpubG9zc3DB9+nS88sor8Pb2xsWLF/Hee++hTp06iIyMNGHWRERERGQOTFrIHj9+HBEREar9ormtMTExWLRoEU6dOoUVK1bgwYMH8PX1RdeuXfHxxx9DJpOZKmUiIiIiMhMmLWTDw8MhRMnD6Tt37qzAbIiIiIjIkljUHFkiIiKiykRqJSC1MnyOrDFiWCIWslqSSqVIS0vT67zKHl/f2IxveOyifsnJyeUWn4iIyFzxN5mWgoODGd8EsRm/bEFBQeUan4iIyFxZ1DqyRERERERFOCJLREREZCK21krIrJUGx1EYIYYl4ogsEREREVkkFrJEREREZJE4tYCILMLSFWtw9362aj9LLkeYxwOjxZdaS+Dk7KLad3d1NFpsIiIqHyxkicgi3L2fjX2pnk+1eJbYVx8RdW9jYtxIo8YkIiqLzFpAZm34GrAKI8SwRJxaQEREREQWiYUsEREREVkkFrJEREREZJE4R1ZLZ86cgUKh0Pk8qVSq1ZOdLDm+vrEZ3/DYAJCSkqJ3fD4VjIjItGysBGysDJ/faowYloiFrJYUCgUCAwN1Pi8tLa3Sx9c3NuMbHrsofkhIiM7xk5OTdT6HiIjInHBqARERERFZJBayRERERFSupk6disuXLxs9LgtZIiIiIhOx/f/ryBq62Zr5OrKbN29G7dq10blzZ6xevRp5eXlGiWvSQvbgwYPo2bMnfH19IZFIsGnTphL7jh49GhKJBPPmzauw/IiIiIjIcElJSfj7778RHByMt99+G97e3njrrbfw999/GxTXpIVsTk4OQkNDsXDhwlL7bdy4EceOHYOvr28FZUZERERExtSkSRN8+eWXuHHjBpYsWYJr166hbdu2aNy4MebPn4+srCydY5q0kI2KisInn3yCl19+ucQ+169fx7hx47Bq1SrY2NhUYHZEREREZGxCCBQUFCA/Px9CCLi6uuKrr76Cn58f1q1bp1Mss54jq1Qq8frrr2PixIlaracJAHl5eZDL5WobERERkTmSQkAqMcIG854jCwAnTpzA2LFj4ePjg3feeQdNmjRBSkoKDhw4gNTUVHz66acYP368TjHNupD9/PPPIZVKdXpRiYmJcHFxUW1+fn7lmCERERERlSUkJAStWrVCeno6lixZgqtXr2LGjBmoU6eOqs/AgQNx+/ZtneKa7QMRTpw4gfnz5+Off/6BRCLR+ryEhATEx8er9uVyOYtZIiIiIhOKjo7G8OHDUb169RL7eHh4QKlU6hTXbEdkDx06hFu3bqFmzZqQSqWQSqW4fPky3n33XdSqVavE82QyGZydndU2IiIiIjKdormwz3r06BE++ugjveOabSH7+uuv49SpU0hKSlJtvr6+mDhxInbu3Gnq9IiIiIgMJrNWGm0zZ9OnT0d2dnax9tzcXEyfPl3vuCadWpCdnY0LFy6o9tPT05GUlAQ3NzfUrFkT7u7uav1tbGzg7e2N+vXrV3SqRERERKQnIYTGqaL//vsv3Nzc9I5r0kL2+PHjiIiIUO0XzW2NiYnB8uXLTZQVERERERmDq6srJBIJJBIJ6tWrp1bMFhYWIjs7G6NHj9Y7vkkL2fDwcAih/XIRly5dKr9kiIiIiMio5s2bByEEhg8fjunTp8PFxUV1zNbWFrVq1ULr1q31jm+2qxYQERERVXZSKwEbK8PXgJUaIUZ5iImJAQAEBASgTZs2Rn+4FQtZLUmlUqSlpel1XmWPr29sxjc8dlG/5OTkcotPRESkD7lcrlo9qkmTJnj06BEePXqksa++q0zxN5mWtH2y2PMY35Jzrwzxg4KCyjU+ERGRPlxdXXHz5k14eXmhatWqGm/2KroJrLCwUK9rsJAlIiIiIqPbu3evakWCffv2lcs1WMgSERERmYjMWkBmbfj81nw9YyxcuBAzZ85ERkYGQkNDsWDBArRo0aLE/g8ePMAHH3yADRs24N69e/D398e8efPQvXv3Yn07duyo8d/GxEKWiIiI6Dm0bt06xMfHY/HixWjZsiXmzZuHyMhInDt3Dl5eXsX65+fn48UXX4SXlxd++eUXVK9eHZcvX0bVqlU1xj916pTWuTRu3Fiv16BXIbtnzx7s2bMHt27dKvZM3KVLl+qVCBERERFVnDlz5mDkyJEYNmwYAGDx4sX47bffsHTpUkyaNKlY/6VLl+LevXs4cuSIavWBWrVqlRg/LCwMEomkzKVWK3SO7PTp0/HRRx+hWbNm8PHx0Thxl4jIEEtXrMHd++qPMsySyxHm8cDg2FJrCZycXYq1u7s6GhybiMjU5HK52r5MJoNMJivWLz8/HydOnEBCQoKqzcrKCl26dMHRo0c1xt6yZQtat26N2NhYbN68GZ6ennjttdfw/vvvw9raulj/9PR0A19N2XQuZBcvXozly5fj9ddfL498iIhw93429qV6PtP67L5+IurexsS4kUaJRURkKCuJNawkxYtAfeIAgJ+fn1r71KlTMW3atGL979y5g8LCQlSrVk2tvVq1ajh79qzGa6SlpWHv3r0YNGgQtm3bhgsXLmDMmDEoKCjA1KlTi/X39/fX89VoT+dCNj8/H23atCmPXIiIiIjIAFevXlVbk1XTaKy+lEolvLy88O2338La2hpNmzbF9evXMXPmTI2F7LN+/PFHLF68GOnp6Th69KjqRrGAgAD07t1br5ysdD3hjTfewOrVq/W6GBERERGVH2dnZ7WtpELWw8MD1tbWyMzMVGvPzMyEt7e3xnN8fHxQr149tWkEQUFByMjIQH5+fql5LVq0CPHx8ejevTsePHigmhNbtWpVzJs3T4dXqE6rEdn4+HjVv5VKJb799lvs3r0bjRs3LvaosTlz5uidDBERERGVP1tbWzRt2hR79uxBnz59ADyp8fbs2YOxY8dqPKdt27ZYvXo1lEolrKyejIWeP38ePj4+sLW1LfV6CxYswHfffYc+ffpgxowZqvZmzZphwoQJer8OrQrZkydPqu2HhYUBAE6fPq33hS3NmTNnoFAodD5PKpVq9WQnS46vb2zGNzx2RcRPSUnROz6fOkZEVDpriQ2sJTZldywzju4/p+Pj4xETE4NmzZqhRYsWmDdvHnJyclSrGAwZMgTVq1dHYmIiAOCtt97CV199hbfffhvjxo1DamoqPvvsM4wfP77Ma6Wnp6NJkybF2mUyGXJycnTOvYhWhWx5PY3BkigUCgQGBup8XlpaWqWPr29sxjc8dkXFDwkJ0Tl+cnKyzucQEVHF6d+/P27fvo0pU6YgIyMDYWFh2LFjh+oGsCtXrqhGXoEnN5Lt3LkT77zzDho3bozq1avj7bffxvvvv1/mtQICApCUlFTsBrAdO3YYNOih881ew4cPx/z58+Hk5KTWnpOTg3HjxnEdWSIiIiILMXbs2BKnEuzfv79YW+vWrXHs2DGdrxMfH4/Y2Fg8fvwYQgj89ddfWLNmDRITE/H999/rHK+Izjd7rVixAo8ePSrW/ujRI/zwww96J0JEREREldMbb7yBzz//HB9++CFyc3Px2muvYdGiRZg/fz4GDBigd1ytR2TlcjmEEBBC4OHDh7Czs1MdKywsxLZt2zQ+zoyIiIiINLOSWMNaoteDVovFMXeDBg3CoEGDkJubi+zsbKPUjVqPyFatWhVubm6QSCSoV68eXF1dVZuHhweGDx+O2NhYnS5+8OBB9OzZE76+vpBIJNi0aZPa8WnTpqFBgwaoUqUKXF1d0aVLF/z55586XYOIiIiIzIeDg4PRBj+1/hNg3759EEKgU6dOWL9+Pdzc3FTHbG1t4e/vD19fX50unpOTg9DQUAwfPhx9+/YtdrxevXr46quvEBgYiEePHmHu3Lno2rUrLly4AE9P4zzlh4iIiIiMr0mTJpBIJFr1/eeff/S6htaFbMeOHQE8WT6hZs2aWidWmqioKERFRZV4/LXXXlPbnzNnDpYsWYJTp06hc+fOBl+fiIiIiMpH0fq0APD48WN8/fXXaNiwIVq3bg0AOHbsGM6cOYMxY8bofQ2tCtlTp06p7Ze2rE7jxo31TqY0+fn5+Pbbb+Hi4oLQ0NAS++Xl5SEvL0+1L5fLyyUfIiIiIkNZSayNMr/VHOfIPv3Y2jfeeAPjx4/Hxx9/XKzP1atX9b6GVoVsWFgYJBIJhBBljsQWPXLMWLZu3YoBAwYgNzcXPj4+2LVrFzw8PErsn5iYiOnTpxs1ByIiIiLS388//4zjx48Xax88eDCaNWum9/KtWt3slZ6ejrS0NKSnp2P9+vUICAjA119/jZMnT+LkyZP4+uuvUbt2baxfv16vJEoTERGBpKQkHDlyBN26dUN0dDRu3bpVYv+EhARkZWWpNkOqfCIiIiIynL29Pf74449i7X/88YfaSli60mpE9umnMPTr1w9ffvklunfvrmpr3Lgx/Pz8MHnyZLX5EMZQpUoV1KlTB3Xq1EGrVq1Qt25dLFmyBAkJCRr7y2QyyGQyo+ZARERERPqLi4vDW2+9hX/++QctWrQAAPz5559YunQpJk+erHdcnRcuS05ORkBAQLH2gIAA/Pfff3onoi2lUqk2B5aIiIjIUllLbGAtsTFKHHM2adIkBAYGYv78+Vi5ciUAICgoCMuWLUN0dLTecXUuZIOCglSPE7O1tQXw5EasxMREnZ+Vm52djQsXLqj209PTkZSUBDc3N7i7u+PTTz9Fr1694OPjgzt37mDhwoW4fv06+vXrp2vaRERERGRC0dHRBhWtmuhcyC5evBg9e/ZEjRo1VCsUnDp1ChKJBL/++qtOsY4fP46IiAjVfnx8PAAgJiYGixcvxtmzZ7FixQrcuXMH7u7uaN68OQ4dOoTg4GBd0yYiIiKiSkbnQrZFixZIS0vDqlWrcPbsWQBA//798dprr6FKlSo6xQoPD4cQosTjGzZs0DU9IiIiIjIDbm5uOH/+PDw8PODq6lrqylf37t3T6xp6Pdy3SpUqGDVqlF4XJCIiIqLKb+7cuXBycgIAzJs3r1yuoVUhu2XLFkRFRcHGxgZbtmwptW+vXr2Mkpi5kUqlSEtL0+u8yh5f39iMb3jsiopf2kNQDI1PRPQ8q8wPRIiJicGUKVMwadIkxMTEAADu378PV1dXo11DIkr7bP//s7KyQkZGBry8vGBlVfLSsxKJxOgPRDCUXC6Hi4sLsrKy4OzsbOp0iEgLM+d9h32pnuUSO6LubUyMG1kusYnI8lVU3VB0nZt3V8HZ2cEI8XLh4z7I7Ooda2tr3Lx5E15eXgAAZ2dnJCUlITAw0CjxtRoyUSqVGv9NRERERFSSZ8dLtRg/1YlWT/YCgGXLluHy5ctGvTgRERERkb60nsQ2ZswY5Ofnw9/fHxEREaqtevXq5ZkfERERUaVV2R+IIJFI8PDhQ9jZ2UEIAYlEguzsbMjlcrV++k6H0LqQffDgAY4cOYIDBw5g3759WL16NfLz81GnTh1VURseHo5q1arplQgRERERVS5CCNSrV09tv0mTJmr7htxjpXUhK5PJVAXrtGnT8PjxYxw9ehT79u3D/v37sWLFChQUFEChUOiVCBE9f5auWIO797OLtWfJ5QjzeGBwfKm1BE7OLmpt7q6OBsclIiLt7Nu3r1zj670+jpWVFaysrCCRSCCRSCCEQM2aNY2ZGxFVcnfvZ5ewOoFxVizgCgVERKbVsWPHco2vdSGbn5+PY8eOYf/+/di7dy/+/PNP+Pv7o0OHDhg5ciRWrlwJPz+/8syViIiIqFKxklgZaR1Zre/fr1S0LmRdXFzg5eWFnj17IjY2FmvXroW3t3d55kZEREREVCKtC9nQ0FCcPHkSBw8eVE0rCA8Ph7u7e3nmR0RERESkkdaF7LFjx5CdnY3Dhw9j3759+OKLLzBw4EDUq1cP4eHh6NixIzp27Kh6ckNlc+bMGb1uZJNKpQgODq7U8fWNzfiGx66I+CkpKXrHDwoK0vk8IiIibel0s5ejoyO6deuGbt26AQAePnyIQ4cOYdeuXRg5ciSys7Mr7aoFCoVCr8eppaWlVfr4+sZmfMNjV1T8kJAQneMnJyfrfA4R0fPGWiKFtUTve+/V4piz4cOHY/78+XByclJrz8nJwbhx47B06VK94uo1M1ipVOLPP//E119/jS+//BLff/895HI5b/YiIiIiomJWrFiBR48eFWt/9OgRfvjhB73jal2+//XXX9i/fz/279+Pw4cPIzs7GzVq1EB4eDi+/PJLREREoFatWnonQkRERESVi1wuhxACQgjVE76KFBYWYtu2bQZNS9W6kG3VqhW8vb0RERGBOXPmICIiArVr19b7wkRERERUuVWtWlX1zIGnn/BVRCKRYPr06XrH17qQTUlJQf369fW+kCYHDx7EzJkzceLECdy8eRMbN25Enz59AAAFBQX48MMPsW3bNqSlpcHFxQVdunTBjBkz4Ovra9Q8iIiIiEzBSmJtpHVkDY9RHvbt2wchBDp16oT169fDzc1NdczW1hb+/v4G1XVaF7LGLmKBJxN8Q0NDMXz4cPTt21ftWG5uLv755x9MnjwZoaGhuH//Pt5++2306tULx48fN3ouRERERGRcRU/2Sk9PR82aNSGRSIwa36S3uEVFRSEqKkrjMRcXF+zatUut7auvvkKLFi1w5coVPg6XiIiIyIydOnVKbb+01WwaN26s1zXMe62GZ2RlZUEikaBq1aol9snLy0NeXp5qXy6XV0BmRERERPS0sLAwSCQSCCFK7SeRSFBYWKjXNSymkH38+DHef/99DBw4EM7OziX2S0xMNGjSMBEREVGFKVQ82YwRx8ykp6eX+zV0Xkf2o48+Qm5ubrH2R48e4aOPPjJKUs8qKChAdHQ0hBBYtGhRqX0TEhKQlZWl2q5evVouORERERFRyfz9/bXe9KXziOz06dMxevRoODg4qLXn5uZi+vTpmDJlit7JaFJUxF6+fBl79+4tdTQWAGQyGWQymVFzICIiIiL9lfXQgyFDhugVV+dCVgih8Y6zf//9V21JBWMoKmJTU1Oxb98+uLu7GzU+EREREZW/t99+W22/oKAAubm5sLW1hYODQ/kXsq6urmoL2j5dzBYWFiI7OxujR4/W6eLZ2dm4cOGCaj89PR1JSUlwc3ODj48PXn31Vfzzzz/YunUrCgsLkZGRAQBwc3ODra2tTtciIiIiMjuKAkCRb5w4Zuz+/fvF2lJTU/HWW29h4sSJesfVupCdN28ehBAYPnw4pk+fDhcXF9UxW1tb1KpVC61bt9bp4sePH0dERIRqPz4+HgAQExODadOmYcuWLQCe3PX2tH379iE8PFynaxERERGR+ahbty5mzJiBwYMH4+zZs3rF0LqQjYmJAQAEBASgTZs2sLGx0euCTwsPDy91SYaylmsgIiIiIssllUpx48YN/c/XppNcLlfdZNWkSRM8evQIjx490ti3rJuxiIiIiOj5UvQpexEhBG7evImvvvoKbdu21TuuVoWsq6srbt68CS8vL1StWlXjzV5FN4Hpu6CtuZNKpUhLS9PrvMoeX9/YjG947IqKX9rTWAyNT0T0XFMqnmzGiGPG+vTpo7YvkUjg6emJTp06Yfbs2XrH1eo3zd69e1UrEuzbt0/vi1my4OBgxjdBbMY3ffygoKByjU9ERJWfUqksl7haFbIdO3bU+G8iIiIiIl0VFhYiOTkZ/v7+cHV11TuOzp/9HTx4sNTjHTp00DsZIiIiIqp84uLiEBISghEjRqCwsBAdOnTA0aNH4eDggK1bt+q9GpXOhaymCz27piwRERERaUGRDyiMcE+BMdaiLUe//PILBg8eDAD49ddfcenSJZw9exY//vgjPvjgA/zxxx96xdX5nXt2QduCggKcPHkSkydPxqeffqpXEuamsLCw3OZyEFkKKysrWFtbmzoNIiKqBO7cuQNvb28AwLZt29CvXz/Uq1cPw4cPx/z58/WOq3Mh+/SDEIq8+OKLsLW1RXx8PE6cOKF3MuagsLAQd+7cMXUaRGbBw8PDKMXs0hVrcPd+drH2LLkcYR4PDI4vtZbAybn4zyZ3V0eDYxMRkeGqVauG//77Dz4+PtixYwcWLVoEAMjNzTXo94zR1sepVq0azp07Z6xwJsORWKL/o1QqjVLI3r2fjX2pnhqOaGrTXUTd25gYN9IosYiIyPiGDRuG6Oho+Pj4QCKRoEuXLgCAP//8Ew0aNNA7rs6F7KlTp9T2ixa0nTFjRrFHyRIRERFRKZ6TdWSnTZuGRo0a4erVq+jXrx9kMhkAwNraGpMmTdI7rs6FbFhYGCQSSbHHx7Zq1QpLly7VOxEiIiIiqrxeffVVAMDjx49VbTExMQbF1LmQTU9PV9u3srKCp6cn7OzsDEqEiIiIiCqnwsJCfPbZZ1i8eDEyMzNx/vx5BAYGYvLkyahVqxZGjBihV1ydC1l/f3+9LmTpbty4odf8WSsrK/j6+lbq+PrGZnzDY1dE/JSUFCgUun9kJZVK+VQwIiICAHz66adYsWIFvvjiC4wc+X/3NDRq1Ajz5s2ruEJ2/PjxqFOnDsaPH6/W/tVXX+HChQuYN2+eXomYO6VSiXr16ul83vnz5yt9fH1jM77hsSsivkKhQEhIiM7xk5OTdT6HiOh5IwrzIRSG31QrCs17HdkffvgB3377LTp37ozRo0er2kNDQ3H27Fm941rpesL69evRtm3bYu1t2rTBL7/8onciRERERFQ5Xb9+HXXq1CnWrlQqUVBQoHdcnQvZu3fvalxL1tnZmeuvEhEREVExDRs2xKFDh4q1//LLL2jSpInecXWeWlCnTh3s2LEDY8eOVWvfvn07AgMD9U6EiIiIiCqnKVOmICYmBtevX4dSqcSGDRtw7tw5/PDDD9i6davecXUekY2Pj8d7772HqVOn4sCBAzhw4ACmTJmCSZMm4Z133tEp1sGDB9GzZ0/4+vpCIpFg06ZNasc3bNiArl27wt3dHRKJBElJSbqmS0RERGS+itaRNcZmxnr37o1ff/0Vu3fvRpUqVTBlyhSkpKTg119/xYsvvqh3XJ1HZIcPH468vDx8+umn+PjjjwEAtWrVwqJFizBkyBCdYuXk5CA0NBTDhw9H3759NR5v164doqOj1e5wIyIiIiLL0r59e+zatatY+/Hjx9GsWTO9YupUyCoUCqxevRp9+/bFW2+9hdu3b8Pe3h6Ojvo9zzwqKgpRUVElHn/99dcBAJcuXdIrPhERERGZXnZ2NqytrWFvb69qS0pKwuTJk7Ft2zYUFhbqFVenqQVSqRSjR49WPZHB09NT7yK2vOTl5UEul6ttRERERFTxrl69itatW8PFxQUuLi6Ij49Hbm4uhgwZgpYtW6JKlSo4cuSI3vF1nlrQokULnDx50mwfjJCYmIjp06ebOg0iIiKisinyASOsIwuFea4jO3HiRDx+/Bjz58/Hhg0bMH/+fBw6dAgtW7bExYsXUaNGDYPi61zIjhkzBu+++y6uXbuGpk2bokqVKmrHGzdubFBChkpISEB8fLxqXy6Xw8/Pz4QZERERET2fDh48iA0bNqBVq1aIjo6Gt7c3Bg0ahLi4OKPE17mQHTBgAACoPdlLIpFACAGJRKL3HAdjkclkkMlkJs2BiIiIiIDMzEwEBAQAALy8vODg4FDq/VG60rmQTU9PN9rFiYiIiKhys7KyUvu3ra2t0WLrXMgac25sdnY2Lly4oNpPT09HUlIS3NzcULNmTdy7dw9XrlzBjRs3AADnzp0DAHh7e8Pb29toeRARERGZhKLASHNk9X/Ma3kSQqBevXqQSCQAntR+TZo0UStuAeDevXt6xdeqkN2yZQuioqJgY2ODLVu2lNq3V69eWl/8+PHjiIiIUO0XzW2NiYnB8uXLsWXLFgwbNkx1vGhaw9SpUzFt2jStr0NEREREFW/ZsmXlGl+rQrZPnz7IyMiAl5cX+vTpU2I/XefIhoeHQwhR4vGhQ4di6NChWscjIiIiIvMRExNTrvG1KmSVSqXGfxMRERERmYrOc2SfV1ZWVjh//rxe51X2+PrGZnzDY1dEfKlUiuTkZJ3jS6X88UJEVCal4slmjDh6WLhwIWbOnImMjAyEhoZiwYIFaNGiRZnnrV27FgMHDkTv3r2xadMmva5tDFr/ptm7dy/Gjh2LY8eOwdnZWe1YVlYW2rRpg0WLFqFDhw5GT9Ic+Pr6Mr4JYjO+6eMHBQWVa3wiIjKNdevWIT4+HosXL0bLli0xb948REZG4ty5c/Dy8irxvEuXLmHChAlo3759BWarmdaPqJ03bx5GjhxZrIgFABcXF7z55puYO3euUZMjIiIiovIxZ84cjBw5EsOGDUPDhg2xePFiODg4YOnSpSWeU1hYiEGDBmH69OkIDAyswGw107qQ/ffff9GtW7cSj3ft2hUnTpwwSlJEREREpDu5XK625eXlaeyXn5+PEydOoEuXLqo2KysrdOnSBUePHi0x/kcffQQvLy+MGDHC6LnrQ+upBZmZmbCxsSk5kFSK27dvGyUpIiIioudCQQGQb4R1ZAuerCPr5+en1lzSkqV37txBYWEhqlWrptZerVo1nD17VuMlDh8+jCVLliApKUnn9IqWWH2WRCKBnZ0d6tSpg969e8PNzU2nuFoXstWrV8fp06dRp04djcdPnToFHx8fnS5ORERERMZz9epVtWmgMpnMKHEfPnyI119/Hd999x08PDx0Pv/kyZP4559/UFhYiPr16wMAzp8/D2trazRo0ABff/013n33XRw+fBgNGzbUOq7WhWz37t0xefJkdOvWDXZ2dmrHHj16hKlTp+Kll17S+sJEVLksXbEGd+9nazyWJZcjzOOBQfGl1hI4ObtoPObu6mhQbCKiysLZ2Vnj/UzP8vDwgLW1NTIzM9XaMzMzNT499eLFi7h06RJ69uypaitaklUqleLcuXOoXbt2idcrGm1dtmyZKr+srCy88cYbaNeuHUaOHInXXnsN77zzDnbu3KnVawV0KGQ//PBDbNiwAfXq1cPYsWNV1fTZs2excOFCFBYW4oMPPtD6wkRUudy9n419qZ4lHC2pXXsRdW9jYtxIg+MQERFga2uLpk2bYs+ePaqHXSmVSuzZswdjx44t1r9BgwbFlmL88MMP8fDhQ8yfP7/YlIZnzZw5E7t27VIrsl1cXDBt2jR07doVb7/9NqZMmYKuXbvq9Dq0LmSrVauGI0eO4K233kJCQoLqiVwSiQSRkZFYuHBhsXkWRERERFQKReGTzRhxdBQfH4+YmBg0a9YMLVq0wLx585CTk4Nhw4YBAIYMGYLq1asjMTERdnZ2aNSokdr5VatWBYBi7ZpkZWXh1q1bxaYN3L59G3K5XBUvPz9fp9eg04rl/v7+2LZtG+7fv48LFy5ACIG6devC1dVVp4sSERERkWn1798ft2/fxpQpU5CRkYGwsDDs2LFDNTB55coVrR+eU5bevXtj+PDhmD17Npo3bw4A+PvvvzFhwgTViPBff/2FevXq6RRXr0fvuLq6qpIgIiIiIss0duxYjVMJAGD//v2lnrt8+XKtr/PNN9/gnXfewYABA6BQPHkKmVQqRUxMjOo5BA0aNMD333+vdUyAj6jV2o0bN1STmnVhZWWl1ZOXLDm+vrEZ3/DYFRE/JSVF9UOnNCWtVUhEROTo6IjvvvsOc+fORVpaGgAgMDAQjo7/d7NuWFiYznFZyGpJqVTqPNwNPFlaorLH1zc24xseuyLiKxQKhISElNlvx55jOudARPTcy1cA+QXGiWPGVq5cib59+8LR0RGNGzc2WlzjTHwgIiIiIirBO++8Ay8vL7z22mvYtm0bCguNcIMbWMgSERERUTm7efMm1q5dC4lEgujoaPj4+CA2NhZHjhwxKC4LWSIiIiIqV1KpFC+99BJWrVqFW7duYe7cubh06RIiIiJKfZBCWUxayB48eBA9e/aEr68vJBIJNm3apHZcCIEpU6bAx8cH9vb26NKlC1JTU02TLBEREZGxFa0ja4zNQjg4OCAyMhJRUVGoW7cuLl26pHcskxayOTk5CA0NxcKFCzUe/+KLL/Dll19i8eLF+PPPP1GlShVERkbi8ePHFZwpERERERkiNzcXq1atQvfu3VG9enXMmzcPL7/8Ms6cOaN3TJOuWhAVFYWoqCiNx4QQmDdvHj788EP07t0bAPDDDz+gWrVq2LRpEwYMGFCRqRIRERGRngYMGICtW7fCwcEB0dHRmDx5Mlq3bm1wXLNdfis9PR0ZGRno0qWLqs3FxQUtW7bE0aNHSyxk8/Ly1NazLHrsGRERERGZhrW1NX766SdERkbC2tpa7djp06e1esytJmZbyGZkZACA6jFpRapVq6Y6pkliYiKmT59errkRERERGYNQFEAUGD7TUyiMsBZtOVq1apXa/sOHD7FmzRp8//33OHHihN7LcVW6VQsSEhKQlZWl2q5evWrqlIiIiIgIT270j4mJgY+PD2bNmoVOnTrh2DH9H6hjtiOy3t7eAIDMzEz4+Pio2jMzM0t9hJlMJoNMJivv9IiIiIhICxkZGVi+fDmWLFkCuVyO6Oho5OXlYdOmTWjYsKFBsc12RDYgIADe3t7Ys2ePqk0ul+PPP/80yuRgIiIiIipfPXv2RP369XHq1CnMmzcPN27cwIIFC4wW36QjstnZ2bhw4YJqPz09HUlJSXBzc0PNmjURFxeHTz75BHXr1kVAQAAmT54MX19f9OnTx3RJExERERmLQgEorMvup00cM7R9+3aMHz8eb731FurWrWv0+CYdkT1+/DiaNGmCJk2aAADi4+PRpEkTTJkyBQDw3nvvYdy4cRg1ahSaN2+O7Oxs7NixA3Z2dqZMm4iIiIi0cPjwYTx8+BBNmzZFy5Yt8dVXX+HOnTtGi2/SQjY8PBxCiGLb8uXLAQASiQQfffQRMjIy8PjxY+zevRv16tUzZcpEREREpKVWrVrhu+++w82bN/Hmm29i7dq18PX1hVKpxK5du/Dw4UOD4pvtHFkiIiIiqhyqVKmC4cOH4/Dhw0hOTsa7776LGTNmwMvLC7169dI7rtmuWmBurKyscP78eb3Oq+zx9Y3N+IbHroj4UqkUycnJZfZ7+kEkRESkpXwFYGOEccV885wjq0n9+vXxxRdfIDExEb/++iuWLl2qdywWslry9fVlfBPEZnzTxw8KCtKq3449+q8DSEREzx9ra2v06dPHoJv4ObWAiIiIiCwSC1kiIiIiskicWkBERERkKvkFgI3EOHGeQxyRJSIiIiKLxBFZItLa0hVrcPd+tsZjWXI5wjweGBRfai2Bk7OLxmPuro4GxSYiosqHhSwRae3u/WzsS/Us4WhJ7dqLqHsbE+NGGhyHiIieDyxkiYiIiEylsBBQFBonznOIc2SJiIiIyCKxkCUiIiIii8SpBVq6ceMGlEqlzudZWVlp9eQlS46vb2zGNzx2RcRPSUmBQvHk0Yd8DC0REZkTFrJaUiqVqFevns7nnT9/vtLH1zc24xseuyLiKxQKhISEAOBjaImIjC6/AJByHVl9cWoBEREREVkkFrJEREREZJFYyBIRERGRRTL7Qvbhw4eIi4uDv78/7O3t0aZNG/z999+mTouIiIjIYKJAabTteWT2hewbb7yBXbt24ccff0RycjK6du2KLl264Pr166ZOjYiIiIhMyKwL2UePHmH9+vX44osv0KFDB9SpUwfTpk1DnTp1sGjRIlOnR0REREQmZNbLbykUChQWFsLOzk6t3d7eHocPH9Z4Tl5entpal3K5vFxzJCIiIiLTMOsRWScnJ7Ru3Roff/wxbty4gcLCQqxcuRJHjx7FzZs3NZ6TmJgIFxcX1ebn51fBWRMRERFpR+QpIR4XGr7lcY6sWfrxxx8hhED16tUhk8nw5ZdfYuDAgbCy0px6QkICsrKyVNvVq1crOGMiIiIiqghmPbUAAGrXro0DBw4gJycHcrkcPj4+6N+/PwIDAzX2l8lkkMlkFZwlEREREVU0sx+RLVKlShX4+Pjg/v372LlzJ3r37m3qlIiIiIjIhMx+RHbnzp0QQqB+/fq4cOECJk6ciAYNGmDYsGGmTo2IiIjIIKKgEEJq+LiiKCg0QjaWx+xHZLOyshAbG4sGDRpgyJAhaNeuHXbu3AkbGxtTp0ZEREREJmT2I7LR0dGIjo42dRpEREREZGbMfkSWiIiIiEgTsx+RNRdWVlY4f/68XudV9vj6xmZ8w2NXRHypVIrk5GQAUHvYCBERGU48LoSQGGGO7OPnc44sC1kt+fr6Mr4JYjO+6eMHBQWp/r1jz7FyvRYREZEuOLWAiIiIiCwSC1kiIiIiskicWkBERERkKopCoMAI44qK53OOLEdkiYiIiMgicUSWiFQ2btmBB/Jc1b61lTUgkaj2s+RyhHk80Du+1FoCJ2eXEo+7uzrqHZuIiJ4/LGSJSOWBPBcH03xK6eFpUPyIurcxMW6kQTGIiIiKsJAlIiIiMhHxuBDCCDM9n9d1ZDlHloiIiIgsEgtZIiIiIrJInFqgpRs3bkCpVOp8npWVlVZPXrLk+PrGZnzDY1dEfCIiInPFQlZLSqUS9erV0/m88+fPV/r4+sZmfMNjV0R8IiIic8VCloiIiMhElPkKKJ9a5tCQOM8jzpElIiIiIovEQpaIiIiILJJZF7KFhYWYPHkyAgICYG9vj9q1a+Pjjz+GEMLUqRERERGRiZn1HNnPP/8cixYtwooVKxAcHIzjx49j2LBhcHFxwfjx402dHhEREZFhCpSAlX4r5xSL8xwy60L2yJEj6N27N3r06AEAqFWrFtasWYO//vrLxJkRERERkamZ9dSCNm3aYM+ePaplgv79918cPnwYUVFRJZ6Tl5cHuVyuthERERFR5WPWI7KTJk2CXC5HgwYNYG1tjcLCQnz66acYNGhQieckJiZi+vTpFZglEREREZmCWReyP/30E1atWoXVq1cjODgYSUlJiIuLg6+vL2JiYjSek5CQgPj4eNW+XC6Hn59fRaVMREREpDXxuBBCGL6OrMgrNEI2lsesC9mJEydi0qRJGDBgAAAgJCQEly9fRmJiYomFrEwmg0wmq8g0iYiIiMgEzHqObG5uLqys1FO0trbW67nyRERERFS5mPWIbM+ePfHpp5+iZs2aCA4OxsmTJzFnzhwMHz7c1KkRERERkYmZ9YjsggUL8Oqrr2LMmDEICgrChAkT8Oabb+Ljjz82dWpEREREBhMKJUSBETaFfp9WL1y4ELVq1YKdnR1atmxZ6hKn3333Hdq3bw9XV1e4urqiS5cuJl8S1awLWScnJ8ybNw+XL1/Go0ePcPHiRXzyySewtbU1dWpEREREFm3dunWIj4/H1KlT8c8//yA0NBSRkZG4deuWxv779+/HwIEDsW/fPhw9ehR+fn7o2rUrrl+/XsGZ/x+zLmSJiIiIqHzMmTMHI0eOxLBhw9CwYUMsXrwYDg4OWLp0qcb+q1atwpgxYxAWFoYGDRrg+++/h1KpxJ49eyo48/9j1nNkzYmVlZXqwQy6nlfZ4+sbm/ENj10R8YmIyHI8+yCoklZzys/Px4kTJ5CQkKBqs7KyQpcuXXD06FGtrpWbm4uCggK4ubkZlrQBWMhqydfXl/FNEJvxTR+fiIjKjyJfAgUMX0dWkf8kxrNr50+dOhXTpk0r1v/OnTsoLCxEtWrV1NqrVauGs2fPanXN999/H76+vujSpYt+SRsBC1kiIiKiSuLq1atwdnZW7ZfX2vozZszA2rVrsX//ftjZ2ZXLNbTBQpaIiIioknB2dlYrZEvi4eEBa2trZGZmqrVnZmbC29u71HNnzZqFGTNmYPfu3WjcuLFB+RqKk+SIiIiInjO2trZo2rSp2o1aRTdutW7dusTzvvjiC3z88cfYsWMHmjVrVhGploojskREREQmoiyUQFlo+BxZfWLEx8cjJiYGzZo1Q4sWLTBv3jzk5ORg2LBhAIAhQ4agevXqSExMBAB8/vnnmDJlClavXo1atWohIyMDAODo6AhHR0eDX4M+WMgSPSc2btmBB/LcUvs8zM5BqEeqal+XH4tSawmcnF1K7ePuapofdEREVFz//v1x+/ZtTJkyBRkZGQgLC8OOHTtUN4BduXJFbYWbRYsWIT8/H6+++qpanJJuKKsILGSJnhMP5Lk4mOZTbvEj6t7GxLiR5RafiIiMb+zYsRg7dqzGY/v371fbv3TpUvknpCPOkSUiIiIii8QRWSIiIiITKcy3gsII44qF+c/n2OTz+aqJiIiIyOJxRFZLN27cgFKp1Pk8KysrrZ68ZMnx9Y3N+IbH1iW+okChcw66yMvLQ3JysmpfKpUiKCioXK9JRETPNxayWlIqlahXr57O550/f77Sx9c3NuMbHluX+LsPHNc5B13IZDKEhISo9p8uaomIiMoDC1kiIiIiEylUSFAoMXwd2UKF4TEsEefIEhEREZFFYiFLRERERBbJ7AvZWrVqQSKRFNtiY2NNnRoRERERmZDZz5H9+++/UVhYqNo/ffo0XnzxRfTr18+EWREREREZTmnnDKWtjeFxrAuMkI3lMftC1tPTU21/xowZqF27Njp27GiijIiIiIjIHJh9Ifu0/Px8rFy5EvHx8ZCUcIdfXl4e8vLyVPtyubyi0iMiIiKiCmT2c2SftmnTJjx48ABDhw4tsU9iYiJcXFxUm5+fX8UlSEREREQVxqJGZJcsWYKoqKhSn3aUkJCA+Ph41b5cLmcxS0RERGapUOaEQltbw+NI8o2QjeWxmEL28uXL2L17NzZs2FBqP5lMBplMVkFZEREREZGpWMzUgmXLlsHLyws9evQwdSpEREREZAYsopBVKpVYtmwZYmJiIJVazCAyEREREZUji6gKd+/ejStXrmD48OGmToWIiIjIaJR2jlAaYUqk0iqv7E6VkEUUsl27doUQwtRpEBEREZEZsYipBUREREREz7KIEVlzYGVlhfPnz+t1XmWPr29sxjc8ti7xC/LLd2mWvLw8JCcnq/Y5n52IiMobf9NoqbS1a5/3+Jac+/MUX2pTvv/dZTIZQkJCyvUaRESVjbBzhDDCHFkhsTFCNpaHUwuIiIiIyCKxkCUiIiIii8RCloiIiIgsEufIEhEREZmIsKsCYWdneBzJ81nSPZ+vmqiS2LhlBx7Ic7Xq+zA7B6EeqTrFt7EGnJyratXX3dVRp9hERESGYiFLZMEeyHNxMM2n3OJH1LmFiXEjyy0+ERGRIThHloiIiIgsEkdkiYiIiExEaWcPpZ294XEgMUI2locjskRERERkkTgiq6UbN25AqVTqfJ6VlZVWT16y5Pj6xmZ8w2NXhDNnzkChUOh8nlQqRXBwcJn9UlJS9I4fFBSk83lERFR5sJDVklKpRL169XQ+7/z585U+vr6xGd/w2BVBoVAgMDBQ5/PS0tK0jq/Po22Tk5N1PoeIiCoXFrJEREREJiLs7CHsDZ8jK4yQiyXiHFkiIiIiskgsZImI6P+1d9/hUZTr38C/s9n0SkJLIJRQJIRQhIgUASM9oIgg9YiA+lNQ6UgEpIgEBQQOIMWjgkeqR+B4VJocQQVREAIhlARDJ4EQJBWSLff7B+/OyUogu8lushu+n+vaC3Z25p47u7Oz9zzzzDNERE7J4QvZK1euYOjQoQgKCoKnpyciIyNx+PDh8k6LiIiIiMqZQ/eR/fPPP9GuXTs8+eST2L59O6pUqYLk5GRUqlSpvFMjIiIiKj13d4i7R+njlHD0HWfn0IXs+++/j9DQUHz22WfqtLp165ZjRkRERETkKBy6a8HXX3+NVq1aoX///qhatSpatGiBjz/++IHL5OfnIysry+xBRERERBWPQxeyKSkpWLFiBRo0aICdO3fitddew5tvvom1a9fed5m4uDj4+/urj9DQ0DLMmIiIiIjKikN3LTAajWjVqhXmzp0LAGjRogVOnDiBlStXYtiwYUUuExsbi/Hjx6vPs7KyWMwSERGRQxIPD4hH6fvIijycfWQdukU2ODgYjRs3NpsWHh6Oixcv3ncZd3d3+Pn5mT2IiIiIqOJx6EK2Xbt2OHPmjNm0pKQk1K5du5wyIiIiIiJH4dCF7Lhx43Dw4EHMnTsXZ8+exfr167F69WqMHj26vFMjIiIionLm0H1ko6KisHXrVsTGxmL27NmoW7cuFi9ejCFDhpR3akRERESlpnh6QvHyLH0ciA2ycT4OXcgCQK9evdCrV6/yToOIiIiIHIxDdy0gIiIiIrofh2+RdRQajQZJSUklWq6ixy9pbMYvfeyyoNVqkZKSUqLlLJ0vISHBbvGJiKji4i+BhUJCQhi/HGIzfvmLiIiwa/zw8HC7xicicmieHjbpIwuOI0tERERE5DxYyBIRERGRU2IhS0REREROiX1kiYiIiMqJxtsTGu/S95HV4OHsI8tClsiBbP16B25l5Vk8f3ZOLppVTrZ4fq0L4O3tY/H8lQL8LZ6XiIiorLGQJXIgt7Ly8GNKsN3idwhLxfChfS2ePzAw0G65EBERlRb7yBIRERGRU2KLLBEREVE50Xh6QGODcWQ1RoMNsnE+bJElIiIiIqfEFlkLXb16FUaj9VcEajQai+7s5MzxSxqb8e+l1+lLlIel9Do9Ll++DMDybScxMRF6vfV5abVai+4KdurUqRLH513BiIgebixkLWQ0GtGwYUOrl0tKSqrw8Usam/Hv9f2+wyXKw1Kubm5qLpZuO3q9HmFhYVavKyUlxeL4kZGRVsdPSEiwehkiIqpYWMgSERERlRMXb0+42GAcWRdhH1kiIiIiIqfBQpaIiIiInJLDF7IzZ86Eoihmj0aNGpV3WkRERERUzpyij2xERAS+//579blW6xRpExERET2Q1ssDWhv0kdUa7TvqjaNyiopQq9WievXq5Z0GERERETkQh+9aAADJyckICQlBWFgYhgwZgosXL9533vz8fGRlZZk9iIiIiKjicfhCtnXr1lizZg127NiBFStW4Ny5c3jiiSeQnZ1d5PxxcXHw9/dXH6GhoWWcMRERERGVBYcvZHv06IH+/fujadOm6NatG7777jvcunULmzdvLnL+2NhYZGZmqo9Lly6VccZERERElnH19LDZoySWL1+OOnXqwMPDA61bt8Zvv/32wPm//PJLNGrUCB4eHoiMjMR3331XovXaisMXsn8VEBCAhg0b4uzZs0W+7u7uDj8/P7MHEREREZnbtGkTxo8fjxkzZuDIkSNo1qwZunXrhuvXrxc5/4EDBzBo0CCMHDkSR48eRZ8+fdCnTx+cOHGijDP/H6crZHNycvDHH38gODi4vFMhIiIicloffvghXn75ZQwfPhyNGzfGypUr4eXlhU8//bTI+ZcsWYLu3btj0qRJCA8Px7vvvotHH30Uy5YtK+PM/8fhRy2YOHEievfujdq1a+Pq1auYMWMGXFxcMGjQoPJOjYiIiKhU7nfNT0nj/PUid3d3d7i7u98zf0FBAX7//XfExsaq0zQaDTp37oxffvmlyHX88ssvGD9+vNm0bt26Ydu2baXMvuQcvpC9fPkyBg0ahIyMDFSpUgXt27fHwYMHUaVKlfJOjYiIiKhE3NzcUL16dTRr1sxmMX18fO65yH3GjBmYOXPmPfPeuHEDBoMB1apVM5terVo1nD59usj4aWlpRc6flpZWusRLweEL2Y0bN5Z3CkREREQ25eHhgXPnzqGgoMBmMUUEiqKYTSuqNbYicfhCloiIiKgi8vDwgIdHyUYbKK3KlSvDxcUF165dM5t+7dq1+96Eqnr16lbNXxZYyFpIo9EgKSmpRMtV9Pgljc3499LZ8Mj8fvFNuVi67Wi1WqSkpFi9LktvJa3VapGQkGC3+EREdC83Nze0bNkSe/bsQZ8+fQAARqMRe/bsweuvv17kMm3atMGePXswduxYddru3bvRpk2bMsi4aPwlsFBISAjjl0Pshy2+1tW+X0mtqxY1a9a0apmIiAg7ZXNXeHi4XeMTEVHRxo8fj2HDhqFVq1Z47LHHsHjxYuTm5mL48OEAgBdeeAE1atRAXFwcAGDMmDHo2LEjFi5ciJiYGGzcuBGHDx/G6tWry+1vYCFLRERE9BAaMGAA0tPT8c477yAtLQ3NmzfHjh071Au6Ll68aHb2rm3btli/fj2mTZuGt99+Gw0aNMC2bdvQpEmT8voToIiIlNvay0BWVhb8/f2RmZlp0c0RdDodbt68WQaZEd3rsy+24McU+42R3CEsFcOH9rV4/sDAQLi6utotHyIiR2Nt3UDly+luiEBEREREBLCQJSIiIiInxT6yRDa09esduJWVV+Lls3Ny0axy8n1f17oA3t4+JY4f4OdV4mWJiIgcDQtZIhu6lZXnUH1ciYiIKjJ2LSAiIiIip8RCloiIiIicEgtZIiIiInJK7CNroatXr8JoNFq9nEajsejOTs4cv6SxH5b49mbvbScxMRF6vd7q+Fqt1qK7gp06darE8XlXMCKihxsLWQsZjUY0bNjQ6uVM97WvyPFLGvthiW9v9t529Ho9wsLCrI6fkpJicfzIyEir4yckJFi9DBERVSzsWkBERERETomFLBERERE5JRayREREROSUnKqQnTdvHhRFwdixY8s7FSIiIiIqZ05TyB46dAirVq1C06ZNyzsVIiIiInIATlHI5uTkYMiQIfj4449RqVKl8k6HiIiIiByAUxSyo0ePRkxMDDp37lzsvPn5+cjKyjJ7EBEREVHF4/DjyG7cuBFHjhzBoUOHLJo/Li4Os2bNsnNWRERERFTeHLpF9tKlSxgzZgzWrVsHDw8Pi5aJjY1FZmam+rh06ZKdsyQiIiKi8uDQLbK///47rl+/jkcffVSdZjAY8OOPP2LZsmXIz8+Hi4uL2TLu7u5wd3cv61SJiIiIqIw5dCH71FNP3XMbyuHDh6NRo0Z466237iliiYiIiOjh4dCFrK+vL5o0aWI2zdvbG0FBQfdMJyIiIqKHi0P3kSUiIiIiuh+HbpEtyt69e8s7BSIiIiJyAGyRJSIiIiKn5HQtsuVFo9EgKSmpRMtV9Pgljf2wxLc3e287Wq0WKSkpVsfXai3bvWi12nsu6rRlfCIiqrj4S2ChkJAQxi+H2BUhvr3ZO/+IiAi7xg8PD7drfCIiqrjKvzmJiIiIiKgEWMgSERERkVNiIUtERERETomFLBERERE5JV7sRQ+1rV/vwK2sPJvFy87JRbPKyepzrQvg7e1js/gBfl42i0VEROTsWMjSQ+1WVh5+TAm2W/wOYakYPrSv3eITERE9zNi1gIiIiIicEgtZIiIiInJKLGSJiIiIyCmxj6yFrl69CqPRaPVyGo3GojsvOXP8ksZ2lPj25syfLQAkJiZCr9dbHV+r1Vp0VzB7xz916lSJ4/OuY0REjo2FrIWMRiMaNmxo9XJJSUkVPn5JYztKfHtz5s8WAPR6PcLCwqyOn5KS4jDxIyMjrY6fkJBg9TJERFS22LWAiIiIiJwSC1kiIiIickosZImIiIjIKTl8IbtixQo0bdoUfn5+8PPzQ5s2bbB9+/byTouIiIiIypnDF7I1a9bEvHnz8Pvvv+Pw4cOIjo7GM888g8TExPJOjYiIiIjKkcOPWtC7d2+z5++99x5WrFiBgwcPWjT0DhERERFVTA5fyBZmMBjw5ZdfIjc3F23atClynvz8fOTn56vPs7Kyyio9IiIiIipDDt+1ALg7nqOPjw/c3d3x6quvYuvWrWjcuHGR88bFxcHf3199hIaGlnG2RERERFQWnKKQfeSRRxAfH49ff/0Vr732GoYNG4aTJ08WOW9sbCwyMzPVx6VLl8o4WyIiIiIqC07RtcDNzQ3169cHALRs2RKHDh3CkiVLsGrVqnvmdXd3h7u7e1mnSERERERlzClaZP/KaDSa9YMlIiIiooePw7fIxsbGokePHqhVqxays7Oxfv167N27Fzt37izv1IiIiIioHDl8IXv9+nW88MILSE1Nhb+/P5o2bYqdO3eiS5cu5Z0aEREREZUjhy9kP/nkk/JOgYiIiIgckFP2kSUiIiIicvgWWUeh0WiQlJRUouUqevySxnaU+PbmzJ8tAGi1WqSkpFgdX6u1bPdSFvETEhLsFp+IiMoP99QWCgkJYfxyiF0W8e3N2d8fe98K2t7xw8PD7RqfiIjKT/k3VxERERERlQALWSIiIiJySixkiYiIiMgpsZAlIiIiIqfEi73ooRbg54UOYal2jU9ERET2wUKWHmrPPt29vFMgIiKiEmLXAiIiIiJySixkiYiIiMgpsZAlIiIiIqfEPrIWunr1KoxGo9XLaTQai+685MzxSxqb8UsfuyziJyYmQq/XWx1fq9VadNcuZ49/6tSpEsfnXceIiEqHhayFjEYjGjZsaPVySUlJFT5+SWMzfuljl0V8vV6PsLAwq+OnpKQ8NPEjIyOtjp+QkGD1MkREZI5dC4iIiIjIKbGQJSIiIiKnxEKWiIiIiJySwxeycXFxiIqKgq+vL6pWrYo+ffrgzJkz5Z0WEREREZUzhy9k9+3bh9GjR+PgwYPYvXs3dDodunbtitzc3PJOjYiIiIjKkcOPWrBjxw6z52vWrEHVqlXx+++/o0OHDuWUFRERERGVN4cvZP8qMzMTABAYGFjk6/n5+cjPz1efZ2VllUleRERERFS2HL5rQWFGoxFjx45Fu3bt0KRJkyLniYuLg7+/v/oIDQ0t4yyJiIiIqCw4VSE7evRonDhxAhs3brzvPLGxscjMzFQfly5dKsMMiYiIiKisOE3Xgtdffx3ffPMNfvzxR9SsWfO+87m7u8Pd3b0MMyMiIiKi8uDwhayI4I033sDWrVuxd+9e1K1bt7xTIiIiIiIH4PCF7OjRo7F+/Xr8+9//hq+vL9LS0gAA/v7+8PT0LOfsiIiIiKi8OHwf2RUrViAzMxOdOnVCcHCw+ti0aVN5p0ZERERE5cjhW2RFpLxTICIiIiIH5PAtskRERERERXH4FllHodFokJSUVKLlKnr8ksZm/NLHLov4Wq0WKSkpVsfXai3bvVSE+AkJCXaLT0RE96dIBT93n5WVBX9/f2RmZsLPz6/Y+XU6HW7evFkGmRE5vsDAQLi6upZ3GkREZcbauoHKF7sWEBEREZFTYiFLRERERE6JhSwREREROSUWskRERETklFjIEhEREZFTYiFLRERERE6JhSwREREROSUWskRERETklFjIEhEREZFT4j0SLXT16lUYjUarl9NoNAgJCanQ8Usam/FLH7ss4icmJkKv11sdX6vVIiIigvFLEf/UqVMlim2KHx4eXqJliYicBQtZCxmNRjRs2NDq5ZKSkip8/JLGZvzSxy6L+Hq9HmFhYVbHT0lJYfxSxtfr9YiMjLQ6NgAkJCSUaDkiImfCrgVERERE5JRYyBIRERGRU2IhS0REREROyeEL2R9//BG9e/dGSEgIFEXBtm3byjslIiIiInIADl/I5ubmolmzZli+fHl5p0JEREREDsThRy3o0aMHevToUd5pEBEREZGDcfhC1lr5+fnIz89Xn2dlZZVjNkRERERkLw7ftcBacXFx8Pf3Vx+hoaHlnRIRERER2UGFK2RjY2ORmZmpPi5dulTeKRERERGRHVS4rgXu7u5wd3cv7zSIiIiIyM4qXIssERERET0cHL5FNicnB2fPnlWfnzt3DvHx8QgMDEStWrXKMTMiIiIiKk8OX8gePnwYTz75pPp8/PjxAIBhw4ZhzZo15ZQVEREREZU3hy9kO3XqBBEp7zSIiIiIyMGwjywREREROSWHb5F1FBqNBklJSSVarqLHL2lsxi997LKIr9VqkZKSYnV8rday3QvjP3iehIQEq2NbGp+IyNkpUsHP22dlZcHf3x+ZmZnw8/Mrdn6dToebN2+WQWZEji8wMBCurq7lnQYRUZmxtm6g8sWuBURERETklFjIEhEREZFTYiFLRERERE6JhSwREREROSUWsn9h6ZXcRA8Dfh+IiMiRcXyWv3BxcUHlypVhNBrLOxWicqXRaODi4lLeaRAREd0XC9kiuLi48AeciIiIyMHxvCEREREROSUWskRERETklFjIEhEREZFTYiFLRERERE6JhSwREREROSUWskRERETklFjIEhEREZFTYiFLRERERE6JhSwREREROSUWskRERETklCr8LWpFBACQlZVVzpkQERGRozPVC6b6gRxbhS9ks7OzAQChoaHlnAkRERE5i+zsbPj7+5d3GlQMRSr4IYfRaMTVq1fh6+sLRVFsHj8rKwuhoaG4dOkS/Pz8GL8M4ztz7ozP+I4c35lzZ3zGLy0RQXZ2NkJCQqDRsAemo6vwLbIajQY1a9a0+3r8/Pzs8oVi/PKNzfiM/zDHd+bcGZ/xS4Mtsc6DhxpERERE5JRYyBIRERGRU2IhW0ru7u6YMWMG3N3dGb+M4ztz7ozP+I4c35lzZ3zGp4dLhb/Yi4iIiIgqJrbIEhEREZFTYiFLRERERE6JhSwREREROSUWskRERETklFjIEpUzXm9ZMd24cQM5OTlOF9skNTUVV69etes6iIhKi4WsE7BXoZOamoqTJ0/aJTYVLz8/HwCgKIpdi1l7xL58+TIOHTpk87iF2fM9OXPmDL799lu7rePSpUto3rw5kpOTnSq2SVpaGiIjI5GUlGS3deh0OrvEPXnyJJYuXWr37ef777+3W/zs7Gy7xQaA06dPY+nSpXaLb9q32UNWVhYKCgrsFp+cDwvZUjAYDHaLnZubi+zsbGRlZUFRFJvHv3LlCiIjIzFt2jQcPnzY5vEvX76MzZs3Y8uWLUhISLB5/LNnz2Lr1q1ltkOz9Y/imTNn8NJLL+GHH34AYPti9s8//8TZs2dx6dIlm28/x48fR3R0NL766itcu3bNprGB/32vTP8ajUabxs/Pz0e/fv2QmJhol+8WAJw7dw6urq6IjIx0qtgmt27dgpeXl93WkZiYiDfffNOmrcoiAr1ej1GjRuHKlSt2+2zz8vLQr18/nDhxwi7xz549i+joaFy5csXm+x3TezRixAi7tbafOnUKr7zyCnJzc23+3T1z5gxiYmLw8ccf27VYJufCQraEkpKSsHjxYqSmpto89smTJ9G3b1907NgR4eHhWLduHQDbFlPJycnIzMxEZmYmli5diiNHjqivlXY9CQkJaN++PebPn49Ro0Zh6tSp+OOPP0qbsur48eNo27Yttm/fjhs3btgsrsnFixfx2Wef4cMPP1RbXWz5o6jT6TB16lSsW7cOa9euxYEDB9R12OIzPnHiBDp37ow+ffogLCwMq1atAmCb7efs2bPo3LkzevfujTlz5qBatWqljllYUlISxo8fj379+mH06NG4ePEiNBqNTX8QtVotdDodatasabOYf2U6ANVobL+LtWfswuswGo1wdXW1aVzTNrh69WrcvHkTPj4+NoutKAq0Wi30ej38/f3N1mdLnp6eyM/PR0hIiM1jA8CePXuQn5+PGjVq2LwYN71HiqIgKCgIgO0PFD/66COkp6fD29vbptuoTqfDtGnTsH//fuzcuROff/45i1kCwEK2RM6ePYs2bdpg0qRJWLp0qU2LqZMnT6JDhw6IiIjAxIkTMXDgQAwfPhzx8fE23ak1bdoUPXv2xIABA3DixAl8+OGHSExMBFC6nf+FCxfQo0cPDBo0CHv37sVnn32GQ4cOISMjwyZ5X7x4Eb1798aLL76I1atXF/ljUpr8ExIS0KFDB3zyySf45JNP0LNnT3z++eelSfkerq6uaN68OXr27Ilff/0VcXFx+OmnnwCUvmBOSkpCdHQ0OnfujLVr12Lq1KkYN24c/vzzT5sUylu3bkXXrl2xcOFCKIqCFStWYMaMGfjwww9L3Tp74sQJtG3bFtnZ2XB1dUVycjL69euHmzdv2vQHMT8/H25ubjYvRLKzs3Hnzh11Hfn5+bhz545NCgV7xja5efMmMjMzAQA5OTnIycmxeaFg2r5v3boFFxcXm8Y2bdv5+fkICAgwW58t5ebmQlEUBAcH2zy2Kb697lhl2l50Oh38/PwA2P49ys3NVWPb8kDC1dUVgwYNgq+vLy5fvowvvvgCX3zxBbsZEAtZa+Xm5iIuLg5PP/00li1bhnnz5uGDDz6wSTF78+ZNjBs3DkOGDMGHH36IwYMHY+HChWjXrh0+/fRTALbZMRgMBhgMBpw+fRoxMTGYNm0akpKSsGTJErRr1w7PP/98iWPv3LkTDRo0wNy5c+Ht7Y0ePXrg0UcfRXx8PD7//HP1VHpJHT9+HE2aNMEHH3ygHqE/++yzePnll9WCs6QF27lz59C7d28MHDgQe/bswb59+zBt2jQsXrwYaWlpNnnvTTG8vb3RunVrbN++HcnJyVi0aBFOnTqFKVOmlLhfoohg6dKl6NixI95//320bNkSr776KqKjo5Geno4zZ84gKyurVPknJSXBx8cHIoIOHTpgzZo1+PnnnzFz5kwMGjRIbV221tWrV/G3v/0NI0eOxKeffooNGzZgxowZuH37tnqAVRpXrlzBjh07AAAajQYZGRk2LQJTU1PRo0cPdRt0cXGBt7c3vLy8oCgK9Hq9WVcJa9Z99epVu8U2SU9PR//+/fHee+8hPz8fPj4+6joA3LOOknwXCnfFKlxsllZycrK6X9Hr9cjMzIS3t7fZPKX97l6+fBn79++HiMDFxcXmXb70er36f0VR1JbwwtNLIzk5GT/99BM0Gg3y8vJw48YNeHp6quuzJTc3N7W111bd70zbdJcuXTB06FC8/PLLqFGjBpYtW8ZilljIWkuj0aBly5bo3r07Ro0ahY0bN2LBggU2KWZ1Oh1u3bqFfv36Afjfl7du3bq4efMmANvsdDQaDapUqYKoqCicOHECzz77LGbOnImtW7ciISEBvXr1KnFsEcHFixcRHx8PAHjvvfewfft2fPnll1i2bBkGDhyINWvWlDj+kSNH1PeiZ8+e2L9/P2rXro0LFy5g0aJFePvttwFY/z7p9Xp89tlnaN68uXoP78qVK6NNmzZITU2FiNjkvTfF6NixIw4fPow6dergX//6F86cOYPu3bvjo48+Un90rf3xVRQF165dg6+vr9kp3F27dqF///54/PHHMXbsWJw6dcrqvPV6PUQE3t7euHPnDvbu3QtfX1/s3LkTe/bsQUpKCtLT0/Hee+9ZHRu4+7kGBARg5MiRau4dO3aE0WgsdSFbUFCA119/HbNnz8aOHTvg6upq0edpzfsfHBwMHx8fLFu2DOvXr0dycjJq1aoF4H+nc00tkBqNBrdu3bI4dkhICLy8vOwS26RKlSpo0KAB9u7diwULFuDo0aOoX78+tFqtOo/p/dJoNFb3r0xJScEHH3ygXqCWl5eHSpUqmc1jMBhKtO0vW7YMTz31FHbt2qXm6+HhYTZPab67er0eQ4YMwcSJE3HgwAGbd+u4cOECunTpor43OTk5aiFuOigv6T4BuPs7snz5cnTs2BE//PADvLy8YDAY1EK2tFJSUvDKK6+oz69fv67GLu37lJubi7y8PGg0GogIfH194e7ujm+++Qbr169HZGQkPvroIxazDzlt8bNQYZ6enhg2bJi6o3n++echIhg0aBBEBFOmTEFQUBCMRiMuXLiAunXrWhy7WrVq+OKLL9CgQQMAd3fsGo0GNWrUwIULF8zmzcnJKXH/MtNO3cXFBXv37kW3bt2wZcsWGAwGhIaG4qeffkLjxo3x2GOPWR27a9eu+Pzzz/H888+jWbNm2LJlC7Zu3Yqnn35aLXTWrl2L3r17IzAw0OofmLZt22Lfvn345JNPoCgKvvjiC9SoUQOZmZlYsmQJtm/fjpMnT6Jx48ZWxdVqtYiMjISHh4fZDv6xxx6Dq6srbty4UeJTiXl5edBqtXBzc1Onubi44OTJk8jKykKTJk1Qr1497NixA+3atVOvWC7Jj2+TJk0wb948+Pv7IysrC+vWrcP69evRsWNH/Pbbb3jjjTcQFRWF8PBwi+LdunULAQEBaoEwYMAAdOzYESdPnkR4eDj8/f1hMBhQuXJlbNq0CS1atMBvv/1m9bZTv359jBw5Eg0bNgRwt3jQarXw9fUt8up2o9Fo8Y+km5sbpk6diqlTp2LRokU4f/48AgMDkZ+fj6NHj8JgMMDd3V09U5Geno6GDRsiLCzMovgGgwEuLi7YsWMH+vfvj7///e8IDAzEf//7X7WrhI+PDwwGA/Lz82E0GlGjRg18+eWX8PX1vW/c1NRUpKeno2nTpti1axf69etns9hFWblyJcaPH49du3bBaDRi//79ePTRR5GdnQ03NzeIiPoeVa1aFbt370ZgYKBFsXft2oXZs2cjLy8P48aNg9FoVFt7TQp3NbBm21+8eDFu376Nfv36YfPmzQgKCsKxY8dQp04dZGRkQKPRwNvbG/n5+bh48SKaNWuGRx991OL4Wq0WH3/8MQYPHoyZM2di+PDh8PDwQFZWFk6cOAGDwWB2FigtLQ316tVD/fr1LYrv6emJP/74A4MGDcJXX32F9PR09dT8X7tfKIqCO3fu3FOoP4hGo8HUqVNx584d9OzZE9u2bUOjRo1w8uRJHD16FDdu3IC7uzu8vLyQl5eHy5cvo0WLFhbvI44dO4Z169YhLy/vnoKyNIXs6dOn8dJLL6FOnTp46623EBYWBm9vb7z//vto1aoVNm3ahI8//hhDhw7FypUrodFoMGjQILt1yyDHpQgHsSwxU6GpKAo2btyIwYMHY+LEiRg7diwWLFiACxcu4J///Oc9O2xLFP6hNo0sYDo1GhcXB3d3d7z55ptmLSaWMrVGrV27FufOncP169exbds27N+/H/Hx8Zg0aRK6dOmCRYsWWbXDNDl37hwOHTqEkydPIjExEV9++aX62vvvv48NGzbg4MGDFsU2FQkmp0+fxlNPPYWAgACEhIRg9+7d6muXLl1Co0aN8I9//AODBg2yKNebN2/i2rVrcHV1RdWqVc36dimKgtzcXISHh2Pbtm3qj9+vv/6K1q1bWxT/xIkTmDJlCiZPnozWrVurO9n8/HwMGjQI69evx6hRo7B7927Mnz8fc+fORVBQEN5///0SHUgAwMyZM5GTk4MjR44gKioK77//vvpaTEwMtFottm3bVmyxEB8fjzfeeAPLly9H06ZNISLIz8/HjBkzsHLlSjz11FPYsmWLOn9iYiKGDBmCr776CvXq1bMo16JaRgtv+927d0eXLl0wYcIEAMD8+fPx/PPPo3bt2sXG/uu2Ex8fjwkTJiA7OxuHDx+Gm5sb/Pz8cOfOHeh0Ori4uMDd3R0uLi749ddfiz0INV2VLSLqdgMAgwcPxsaNG/H444+jQ4cO8Pb2VgvBvLw8+Pj4ICYmBhEREfeNfeXKFTRr1gwdOnTApEmT0KZNGwDAwIEDsXnz5lLFNrl8+TIOHDgArVaLunXrokWLFgCAyZMnY926dahevTr69euH6tWrq59RXl4evLy88Pjjj6NRo0bFrqOwpUuXYt68eXjjjTewefNmhISEoFu3bsjOzoaLiwtcXFxgMBhw48YNNGjQwKyVzxLDhw/H2rVrAQCBgYHw9fVFRkYGFEVRP2cRwcGDBy0qMtPS0uDi4oLAwEC4uLggJSUFffr0AQB1xILg4GDk5ubi9u3b8PDwUA9WDx8+bNE2anLt2jV07doV3t7eqF+/Pn755Re0bt0aBQUFCAwMREFBAXJycmA0GlGvXj3MmTPH6gvx0tPTERsbq3ZTq1OnDvLy8pCVlQVXV1d4eXnh9u3bcHV1xcGDBy3+Dufm5uLbb7/F+PHj0aFDB7i4uEBE0KRJEwBAQEAACgoKkJubizt37uCRRx7B4MGDHxhTp9NhwIAB2LZtG0JDQyEi6Nu3Lxo0aIDRo0djypQp0Ol0WLhwIW7fvo0RI0bg8OHDmDFjBoYOHWrV+0IVgFCpGI1GMRgMIiKyceNGcXV1lUceeUS0Wq0cPXq01LFFRKZOnSo9evQQEZHp06eLoigSHx9fqtgiIvv27RNFUaR69epy+PBhdfrWrVslJSWl1PE//vhjiYmJkfz8fHXauHHj5JlnnpGcnJxilz9z5owsWLBArl69ajb9m2++Ea1WK1WrVpUDBw6o0/Pz8yU6Olp27NhhUX4JCQnSokULadKkibi7u8u7774rBoNB/Tx1Op2kpaVJSEiInDp1SkREYmNjRVEUuX79erHxT5w4IQEBAfJ///d/cvHiRbPXjEajdOrUSby9vaV69epy6NAhERH5/fffJSoq6p75i5KSkiIffvihjB8/XjZu3HjP6/3795elS5eKiEhBQYGIiPTt21diY2PVbet+4uPjxdXVVSZNmnTPa8eOHZORI0eKoigydepUuX79uty6dUtmz54tTZo0kWvXrhWb+40bN9T/m97vonTu3Fnef/99Efnftn/s2LFi499v2zl27JhER0dLq1atZNasWXL16lW5du2aJCYmyh9//CHXrl27Z5miJCYmSteuXaVFixYSEhIiX3zxheh0OvX1wYMHS2RkpGzcuFHu3LlTbLy/+uGHH0Sr1Up0dLS88MILcvDgQfW1AQMGSHh4eIlji4gcP35cateuLa1atZJq1apJ79695fTp0+rrEydOlMcee0zmzp0rt27dKtE6kpOTZcuWLZKXl6dOW7BggVSrVk20Wq0EBATIU089JQ0bNpRHHnlEWrduLc2bN5eIiAhJTEx8YNzp06fL0KFDZfXq1ep0vV4vkydPFkVRZNWqVXLt2jVJTU1VP+Nr167JzZs3Lcr9+PHjUqdOHVm4cKFkZ2eLXq8XkbvfuaioKGnUqJHMnz9fkpOT5dKlSxIfHy/Jycly+fJli7YfkbvbfeHvYWpqqrRp00YURZEnnnhCBg0aJE8//bQMGjRIBg4cKM8++6wMHTrUon3/xYsXZe3atfLRRx/J9u3b1elpaWkyZcoUURRFPvnkE8nNzZUrV67I1atX5cqVK5Kammr23byftLQ0s+e5ubmyYcMGadSokSiKIs2bN5c2bdpI3bp1pXHjxhIRESERERHSvHlzOX78uEXvz7Fjx6Rr167y4osvyhtvvCHLli2TGjVqyIsvvigDBw4URVHkhx9+EBGR27dvy4svvmiT3y1yPixkbcBoNKo7pOjoaAkMDLT4y/ogph/4GTNmyCuvvCLz588Xd3d3+f3330sdW+RucfPJJ5+ohUFxxY21EhMTxd/fXz744AP5/PPPZfLkyRIQEGDRe5OcnCyBgYGiKIrExsZKenq62esbNmwQjUYj3bp1kw0bNkhycrJMmTJFQkJCLCoCExMTJSgoSCZOnCiJiYmyYMECURTFbFmj0SjXr1+XkJAQSUlJkdmzZ4uPj4/89ttvxcbPycmRrl27ymuvvaZOO3XqlBw9elTOnTsnIiJr1qyR7t27qwcRps/bkuLk+PHjUrNmTXnqqaekbdu2otFo5IMPPjCb580335SQkBA5d+6cnD59WmbNmiVVqlRRi/L7OXHihHh6eso777yjvg8ZGRly9uxZdZ7z58/LnDlzxMPDQ+rUqSNNmzaV4OBgOXLkSLG5JyYmiouLi4wePVqd9tdtz1Q4tGnTRlauXClLliyxeNsvbtsxFbM9evSQ//znP+r0BxXUf80/KChIxo0bJ+vWrZPx48eLq6vrPQeuzz33nDRu3Fj++c9/SnZ2tkWxTTIyMuTpp5+WVatWyaOPPipDhgwxiz98+HBp1KhRiWKfP39eatSoIVOmTJGcnBz57rvvpHr16vLrr7+azTdmzBh59NFH5d1337WouCns2LFjUqVKFXn55ZflypUrZu/tqlWrpGrVqjJ27Fj1+2Y60DIajer/7xc3ODhYYmJi5JlnnhEXFxdZsWKF+rpOp5MRI0ZIQECAWQFnjTNnzkhQUJBMmDDhnm1HROTs2bMSGRkpPXv2lH379lkdPzk5Wd2H/LWYvXr1qrRv314iIyMlNTW1RPkfP35catWqJU888YTUr19fatSoIcuXL1dfv3btmrz88svi5eUl33//vTrd0u0/OTlZFEWR5557zmx6Tk6OrF+/Xpo1ayYxMTHq9MKfZ+GDmqKYvvcmv//+u0RHR8szzzwjO3bsUA+Yhw4dKoqimOVPDy8Wsjai1+tl3LhxFrcYWWPOnDmiKIr4+/urLXe2YunOq6T++9//Sr169aRBgwbSqVMni96bnJwcGTFihLz44ouyfPlyURRFJk2adM+Pyvfffy9t2rSRatWqSaNGjaRhw4YWFVLp6enSoUMHGTNmjDrNaDRK9+7d5cCBA3L06FG5dOmSiNwtKiMiIqRz587i5uZm1nL9IHfu3JH27dvLkSNHRK/XS7du3SQqKkp8fX2ldevW8vnnn4uIFFkgFHdAcf78ealfv75MnjxZ/fw++eQTqVatmiQlJanTTp06JdHR0aIoikREREijRo2KPUtw48YNqV+/vrRo0UKdNnz4cGnZsqUEBwdL+/btzVqEkpKS5J///Kds27ZNzp8/X+z7cuXKFXnsscekVatW4uPjI2+88cYD/+6nn35aAgICxNvb2+IDCEu2naNHj0p0dLTExMTI5s2bi41rkpGRIV27dpU333zTbHqnTp3Uv6XwGYhBgwZJcHCwbNq0yeJ16PV6uX79ujRs2FAuX74sW7ZskaioKHn55ZclKipKRowYISJ3W2ZDQkKsii1yt5Ds1KmT2fvds2dPWbVqlaxdu1Z27dqlTp84caKEhYXJ+++/b/G+4sKFC1KrVq0iW/NN5s+fLzVr1pSpU6dadOApcreAqlWrlsTGxqq5vPzyyzJt2rR75n3xxRelUqVK8vXXX1sUu7CJEyfKoEGDROTu/vHf//63zJ8/X/bs2SMXLlwQkbvFbtOmTaVHjx4WnwEyLefp6WnWmlhUy2yTJk2kadOmaguj6fXi9g0pKSlSu3ZtmTx5sty5c0fOnTsn06ZNk65du5rtazIyMmTkyJHi4+NjVf4iIj/99JMEBwdLpUqVpFevXmavZWdny8aNG6VatWoyYMAAdbrp4PxB+d/vLMqRI0ckOjpaOnfurL5npulEIixkbUav18s//vGPUncnKMqhQ4dEUZQHnm5zZBkZGZKWliZ//vmnRfPn5eXJ8uXL1dPlmzZtum9BcuPGDUlKSpKjR48W2XpSlBs3bsjcuXMlKSlJnTZ79mz1lFjNmjWlW7dusm/fPrl+/booiiLu7u5WHaCkpaVJlSpVZNeuXTJu3Djp1q2bHDt2TLZv3y4TJ06UatWqyVdffWVxPBODwSDz5s2T7t27m53yNbXQFj49LHL3B2Tbtm3y888/W3zK8/XXX5f27dvLjBkzJCoqSrp37y6rV6+WrVu3Sps2baRWrVqSnJxcotzXrVsn/fv3l/3798umTZvE09PTrJj9a7H0/PPPi4eHhyQkJFi0Dmu2nWPHjknLli2lb9++FrdqpqWlyWOPPSY//vijWb7Dhw+XIUOGqPMVblkaNmyY/PHHHxbFF/nfj/2QIUPUIuPbb7+VypUri6+vr3z88cfqvC+88IJVsUVEVq5cKWFhYWohYDpQ7ty5s0RFRUnVqlXN1jFt2jSrTtn+5z//kZ49e4rI3da4qVOnSp8+feSll16STz/9VJ1vwYIFUrt2bRk3bpxcvnz5gTF1Op1MmDBBXnrpJbl9+7Y6fejQodKjRw/p3r27vP3222b73/79+0toaKhF3ZgK6969u3z44YciItKuXTtp27at1KxZU5o0aSJdu3aVEydOiMjdltnQ0FDp27dvsS2NIncPoHv16iUxMTEyePBgqVSpkuzZs0dEii5mW7ZsKXXq1FGL5+LodDp555135JlnnpHc3Fx1+rfffisBAQHqwblJRkaGDBgwQKpXr242/4MYjUb57bffpH379vLDDz9ISEiIPPPMM+rrpm5FGzdulLCwMLOW2Qcp7ixKfHy8REdHS7du3WTr1q3qdHs3xJBzYCFrQ7Y+NV+YtTtjZ/fXv3fjxo2iKIpMnDhRbVnQ6XTqaXprZWVlqf/fsGGDKIoimzZtkoyMDNm3b59ERUXJjBkzRERk0aJFVh9EGI1GGThwoLz++uvSq1cvs1aPS5cuydChQ+XVV18VvV5v9Xazb98+mTJlitk0g8EgderUUVssSrItFv5RGD9+vFSrVk1iYmLu6Q8XEREhw4YNsyq2qbC7cOGCWSvZhg0b7ilmC/c7/+WXXyxq6S3Mkm2noKBAcnNz5fz581bHL3wAZDptOm3aNPnb3/5mNp+l/THv54UXXlA/55EjR0qlSpWkcePGMmLECPn5559LHDclJUXatm0r9evXl+eee04URZFt27aJ0WiUa9euyZtvvimdOnW653O31KxZs+Txxx8Xkbt9nDt16iRjxoyRLl26SPPmzWXy5MnqvO+99540btzYoj7nycnJsnfvXvX5nDlzRKPRyOjRo2XWrFlSuXJlefbZZ81OZVt68FbYyy+/LGPGjJG5c+dK165d5fLly2IwGGTLli3SrVs3efHFF9UDn3Pnzll8IHH8+HEZMmSI7Nq1S5KTk2X48OFSqVIl9fT4X0+rm7oZWBpfp9PJunXrZMmSJWbT09PTJTQ0VE6ePHnPMunp6Va/RwUFBdK9e3c5f/687Ny5U6pWrSrPP/+8vPTSSzJ+/HjR6/WSl5cna9eulSZNmhR7kGLpWRRTMdurVy/58ssvrcqZKjYWsuTQChd6poJz0qRJcuXKFRk3bpz07dtXcnJySnUQcf78+Xv6XsbExEjv3r1FpORH/YcOHRJvb29RFOWeU5wTJkyQDh06WJz3X3/kTEzLGwwGqVu3rtlp4e+//96iC69ycnIkKytLMjMzzaYvWLBAvvrqK3Udphyee+456devn0V5i9w9jR8TE1PkwZher5eNGzeaFbN6vV7Wrl1bbF/e4jxo2xk7dqw888wzZt0ArFV4u5g6dap069ZNfT537lxZuHChFBQUWL1tmuZfs2aNzJgxQ1577TUJDg6WlJQU2bJli9SrV09effVVuX37dom3+5SUFNm0aZPMmDHjns9y3rx50qxZM7OWT2vs3r1boqOj5R//+Id06dJFLWRu3bqlFrmFz25kZGTcN1ZGRoacPHnynjMN586dkyFDhpj1gz148KAoiiK//PKLVfnm5uaabQdxcXHSrFkzee6552TevHlm8y5ZskTq1Klj8dmfvyp8fcCZM2fULhC7d+8WkbvblF6vV/O53/e+sOvXr6vbQeH30jQtLy9P6tata9YlqDQHQrm5udKsWTPZtm2biNz9fpv2c4X/vry8PLMGg/ux91kUqvhYyJLDs+fIEH9lMBjk9u3bMmDAAJkzZ06p4/3444+iKIr06tVLPSUpcvdCrJdeeumBF7aYFNV3rHABo9PpJCcnR+rXr69e3W4aXeHKlSsPjF3U1feFfzz/WugZjUbp16+f2YVgDxIfHy+enp7y1ltvmcUoXATqdDqzbgZvvvmmaLVaq1tKi/KgbccWfezsObKIvUcVESn9yCIi9xZbp06dkpCQEGncuLF07tzZ7LWLFy+Kl5eXrF+/Xp12v23INKpIZGSkuLm5ybvvvmu2LlOLt+kz/umnn6Rp06ZWbTcJCQkSExMj+/btM+se8MQTT4iiKPLCCy+YfUePHDkijRs3vuc0/f0YjcYHFqNJSUlqMWtqmZ04caKsW7fOooOUhIQEeeSRR2TZsmVm8xc+wE1NTZWqVauq+5+pU6eKoihy7do1qw+ETH/LqFGj5F//+peI3B2hIygoSIKCgqw6wC3M3mdRqGJjIUtOwV4jQxRl+vTpUqtWLbNTyKWxb98+CQkJkccee0xGjhwpf/vb38Tf39+ifp/F9R0T+V/xXa9ePTl8+LDMnj3booujLL363kSn08m0adMkODjYoj6yx44dE29v73su+imqFVSv18v69etFURSpVKmSxRfVWcKe2449Rxax96giIqUbWUTEfkPkWTqqSGFTpkyRjh07Wtyl40HD46Wlpcnjjz8uPj4+snLlSrWl86233pJWrVpZNCTZmTNnZMyYMRITEyOzZs2678gPpmK2atWq0qtXL4svGD516pRUqlRJxo8fX2Q/WlOBn5aWJtWqVZPz58/Le++9Jz4+PhZdNHzhwgX59NNPZeHChWqLscmcOXNk7Nix8sILL6gHWj///LO4uLjI4MGDi419P/Y+i0IVEwtZchr2HBlCRGTz5s0yevRoCQoKsvkVsadPn5Zp06ZJ586d5bXXXrOoiLW075hJixYtJCoqStzc3Ir9obLk6vvChcKuXbukd+/eUr16dYvem9TUVKlevbp6ul2v18vYsWMlJiZGGjVqJIsWLTLrOqDX62XkyJHi6+tbZF++0rL3tmOvkUXK4mKWkowsImK/IfIsGVWk8PJnz56VadOmia+vr8UFuCXD4+Xk5Eh0dLQ0aNBAqlevLl26dJGgoCCLzgIdP35cqlatKv369ZP/+7//Ezc3N5k5c6bZPIU/28TERAkNDZXAwECLWvINBoO88sorMnz4cPX5jz/+KJ9++qmcOXPG7MLavLw8adasmXTr1s2ifYMp/9q1a0u7du2kcePG4urqKmvXrlVf37Rpk3h5eUmDBg3MDtp++uknOXPmTLHxH8TeZ1Go4mEhS07DniNDiNxtoXn++eftUkiZFL7hQnEs7Tum1+slIyND/P39xcXFxaIfc0uvvhe5+8OSnJwsb731lsX9VlNTU+XZZ5+VVq1aybZt26R79+7y1FNPyYQJE2T06NFSt25dGTlypNqStH37dqlXr57Nh5czsfe287CNLGLPIfIsHVXk559/lj/++EMGDBggDRs2tOqzLW54vJUrV6rz7tixQxYtWiSfffaZRRdepaSkSJ06dSQ2NladNnPmTBk1atQ9XYlMLZBjx44VV1dXi0fn0Ov10r59e7W47Nixo7Rs2VL8/f2lXr16Zq3Mly5dEkVRxM3NzaKDFNMQXm+99ZbcuXNH0tPTZdasWdKiRQt1bNs7d+5IbGysTW7MU5SyPANHzo+FLDkVe44MISIW9VktS5aO3pCeni47duww64dbHEuvvjcNzWPJhSeFXb16VV544QXx9PSULl26mJ1aXbdunQQEBMh3330nIncL65IOAG8pe287D9PIIvYeIs+SUUVmzZolBQUF8tNPP1k8RJXJg4bHmzRpklSvXl02bNhgVUyRu9+R+fPny2uvvWZ28eRLL70kbdq0kaioKHn11VfNLv48c+aMxMTEWN3a2LdvX1myZIlMnz5dunbtKmfPnhWdTieLFy+Wtm3byqxZs8RgMEheXp7ExcVZdBBaUFAg06dPl2eeecasz/CuXbukevXqJRoFoqTsfRaFKg4WskROoLjRG5599lmLx4L8q+Kuvl+wYIHZ7VetceXKFYmNjVXHyyxcTNavX18mTpxYorhU/uw9RJ5JcaOKlISlw+PpdDqru3dcunTJbOSEd999V1xcXGTq1Kny97//XaKioiQ6OtrswM2aK/BN+bz66qvSvHlzGTJkiKxatcpsnokTJ0p4eLh6gGrJAbrpQHXz5s3y3nvvmb1269YtCQ0NLdNWUXufRaGKQwsicnguLi4QERiNRgwcOBCKouBvf/sbvv76a5w9exaHDx+Gl5dXiWJrNBqICBRFUZ8DwDvvvIM5c+bg6NGj0GpLtqsICQnBlClT4OHhAQBQFAUigps3b6JKlSpo0aJFieJS+fP29gYAGAwGaDQaDBgwACKCwYMHQ1EUjB07FgsWLMCFCxfw+eefw8vLS93GrFG7dm3Url0bAGA0GlFQUAAfHx80bdq0xLkrioIJEyagU6dOyMvLwyuvvKK+VrNmTVSrVg2HDh2Ci4uLRTkbDAa4uLioy9esWRMAkJGRgYyMDHzzzTfo3r07AKBz586IiIhAfHy8Os3Hx+eB8XNzc2E0GiEi8PPzAwAsXLgQbdu2xfr169X3x6Rr167YvXs3cnJyUKlSJbi6uj4wfnx8PKZPn47Nmzejd+/e6vfVtF8wff91Op26zK+//orWrVsX+96UlIuLC0aMGFGibYYeLpryToCILKMoiloIDhgwAE888QTS09Nx9OhRNG/evFSxRQQAoNVqERoaigULFuCDDz7A4cOH0axZs1LF9vPzg5ubm/pcURT8/e9/x40bN9CuXbtSxabyZyrgTAdZGzZswOLFixEdHY2lS5di+vTp8Pb2tklBotFoMHfuXPzyyy/o379/qWK1atUK27dvBwCsXr0aiYmJ6ms6nQ4NGzaEXq8vNk5SUhIWL16M1NTUe14LCgrCe++9h+7du6sHonq9Hi1atECNGjUsyvPkyZPo27cvOnbsiPDwcKxbtw4GgwFeXl5YtWoVIiIisGHDBuzcuRO5ubkAgJ07dyIgIADu7u7Fxj927Bjatm2Lxo0bw9PTUy1iDQYDFEWBTqdDTk6Ouk4AePvtt9GmTRukp6db9DeUFItYski5tQUTUYnYs++Yva6+N9mwYYO88sorUqlSJV6BXMHY+wIde40qYq/h8UzvxV/7Zr/99tvSunVri+5mdr8h8kx/v8FgkBMnTkiLFi2kVq1a0qxZM+ndu7cEBARYdCGWJUPkGY1GSU9Pl5CQEElJSZHZs2eLj49PscP7EZUVFrJETsaefcfsffX9sWPHJCYmxqqL0sh52PMgy56jipTF8HiJiYkybdo08fPzs+i9sXaIvNWrV8s777wj8+bNs2gILEuGyDPdUe3OnTvSpEkT6dy5s7i5udl0nGei0lJE/v85RSJyGlKoT6ut5ebmqv0f7aGgoMCsqwFVHAaDAWvWrEHLli1L3d2lKDqdrtj+nqVhNBoB/K+f+IPcvn0bn332GYKCgjBgwABs3rwZAwcOxMSJEzF58mRUrlxZnffixYsYO3YsTp8+jQ0bNljUXefatWt4+umnsWDBAjzxxBMwGo3QaDQYMWIECgoK8MUXXwAw759rjbS0NIwaNQqXLl3CtGnTsHLlSuh0OjRv3hx37tzBd999h+joaEybNg1arRa1atWCm5sbfvvtt1L1TyayNRayRERkM/Y8yHI0fz3o27RpEwYNGoQJEyZgypQpCAoKgsFgQEZGBgoKCgBAvRDMEsnJyWjQoAGA/xXx06dPVy+gM8nOzoavry8A697/1NRUTJkyBV9++SXat2+PDRs2ICgoCACwfv16jB49Gl988QViYmKwZMkSdOnSBY0bN7Y4f6KywFELiIjIZh6WIhawfOSGc+fOYcOGDeqFVJYyFbFGo1FtiRYRXL9+XZ0nLi4O7u7uePPNN6HVaq16/4ODgxEXF4caNWqgc+fOCAoKUgvhwYMHY8aMGdi3bx9iYmLw+uuvl6jll8jeWMgSERGVwoOGx/vjjz/w22+/WV3EFlaeQ+SZuhGwiCVHxeG3iIiISul+w+MdOXLEJuMlSzkNkffEE0+UKjaRvbFFloiIyAYURYHBYMCkSZPwww8/ID4+HpGRkTaJbWqFdXV1xccffww/Pz/8/PPPePTRR20S32Tjxo344Ycf8OWXX2LPnj333GyByNGwRZaIiMiGIiIicOTIEbtc3d+tWzcAwIEDB9CqVSubx2/cuDGuXLmCn376iXfeI6fAUQuIiIhsyN4jN3CIPKL/YSFLRERERE6JXQuIiIiIyCmxkCUiIiIip8RCloiIiIicEgtZIiIiInJKLGSJiIiIyCmxkCWiMqUoCrZt22bXdezduxeKouDWrVt2XY+91KlTB4sXLy7vNIiIHB4LWSKymbS0NLzxxhsICwuDu7s7QkND0bt3b+zZs0edJzU1FT169LBrHm3btkVqair8/f0BAGvWrEFAQECxy61Zs0a91aiLiwsqVaqE1q1bY/bs2cjMzLR5npbmRUREReMtaonIJs6fP4927dohICAA8+fPR2RkJHQ6HXbu3InRo0fj9OnTAIDq1as/MI5Op4Orq2upcnFzcyt2Pffj5+eHM2fOQERw69YtHDhwAHFxcfjss8+wf/9+hISElCo3IiKyHbbIEpFNjBo1Coqi4LfffsNzzz2Hhg0bIiIiAuPHj8fBgwfV+Qp3LTh//jwURcGmTZvQsWNHeHh4YN26dQCATz/9FBEREXB3d0dwcDBef/11s2Xi4+PVmLdu3YKiKNi7dy8A864Fe/fuxfDhw5GZmam2ts6cOfO+f4eiKKhevTqCg4MRHh6OkSNH4sCBA8jJycHkyZPV+YxGI+Li4lC3bl14enqiWbNm+Ne//qW+bsrh22+/RdOmTeHh4YHHH38cJ06cUF9/UF55eXkYMWIEfH19UatWLaxevbokHwsRUYXGQpaISu3mzZvYsWMHRo8eXeStM4s7fT5lyhSMGTMGp06dQrdu3bBixQqMHj0ar7zyChISEvD111+jfv36Jcqtbdu2WLx4Mfz8/JCamorU1FRMnDjRqhhVq1bFkCFD8PXXX8NgMAAA4uLi8Pnnn2PlypVITEzEuHHjMHToUOzbt89s2UmTJmHhwoU4dOgQqlSpgt69e0On0xWb18KFC9GqVSscPXoUo0aNwmuvvYYzZ86U6D0gIqqo2LWAiErt7NmzEBE0atSoRMuPHTsWffv2VZ/PmTMHEyZMwJgxY9RpUVFRJYrt5uYGf39/taW1pBo1aoTs7GxkZGTA398fc+fOxffff482bdoAAMLCwvDzzz9j1apV6Nixo7rcjBkz0KVLFwDA2rVrUbNmTWzduhXPP//8A/Pq2bMnRo0aBQB46623sGjRIvzwww945JFHSvw3EBFVNCxkiajURKRUy7dq1Ur9//Xr13H16lU89dRTpU3Lpkx/o6IoOHv2LPLy8tQC1aSgoAAtWrQwm2YqdAEgMDAQjzzyCE6dOlXs+po2bar+31TsXr9+vTR/AhFRhcNClohKrUGDBlAURb2gy1qFuyN4eno+cF6N5m6PqMLFs06nK9F6rXHq1Cn4+fkhKCgIKSkpAIBvv/0WNWrUMJvP3d3dJuv76wVviqLAaDTaJDYRUUXBPrJEVGqBgYHo1q0bli9fjtzc3Htet2Y8V19fX9SpU8dsyK7CqlSpAuDuMF4mhS/8Koqbm5vat7Ukrl+/jvXr16NPnz7QaDRo3Lgx3N3dcfHiRdSvX9/sERoaarZs4Qvd/vzzTyQlJSE8PNwmeRERPezYIktENrF8+XK0a9cOjz32GGbPno2mTZtCr9dj9+7dWLFihUWn001mzpyJV199FVWrVkWPHj2QnZ2N/fv344033oCnpycef/xxzJs3D3Xr1sX169cxbdq0B8arU6cOcnJysGfPHjRr1gxeXl7w8vIqcl4RQVpamjr81i+//IK5c+fC398f8+bNA3C32J44cSLGjRsHo9GI9u3bIzMzE/v374efnx+GDRumxps9ezaCgoJQrVo1TJ06FZUrV0afPn2szouIiO7FFlkisomwsDAcOXIETz75JCZMmIAmTZqgS5cu2LNnD1asWGFVrGHDhmHx4sX46KOPEBERgV69eiE5OVl9/dNPP4Ver0fLli0xduxYzJkz54Hx2rZti1dffRUDBgxAlSpV8MEHH9x33qysLAQHB6NGjRpo06YNVq1ahWHDhuHo0aMIDg5W53v33Xcxffp0xMXFITw8HN27d8e3336LunXrmsWbN28exowZg5YtWyItLQ3/+c9/4ObmZnVeRER0L0VKe5UGERHdY+/evXjyySfx559/8u5dRER2whZZIiIiInJKLGSJiIiIyCmxawEREREROSW2yBIRERGRU2IhS0REREROiYUsERERETklFrJERERE5JRYyBIRERGRU2IhS0REREROiYUsERERETklFrJERERE5JRYyBIRERGRU/p/mtagbJYeefIAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 680x791.818 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(1, \"bernstein-vazirani\")\n",
+    "import bv_benchmark\n",
+    "bv_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
+    "                max_circuits=max_circuits, num_shots=num_shots,\n",
+    "                method=1,\n",
+    "                backend_id=backend_id, provider_backend=provider_backend,\n",
+    "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
+    "                api=api)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quantum Fourier Transform - Method 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Quantum-Fourier-Transform (1) Benchmark Program - Qiskit\n",
+      "... execution starting at Sep 29, 2024 05:41:54 UTC\n",
+      "... configure execution for target backend_id = nvidia\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 2\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 2 qubit group = 16, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 2 qubit group = 16, 0.5, 12.0\n",
+      "Average Creation, Elapsed, Execution Time for the 2 qubit group = 0.0, 0.06, 0.059 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 2 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 3\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 3 qubit group = 36, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 3 qubit group = 36, 0.5, 27.0\n",
+      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 3 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 4\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 4 qubit group = 64, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 4 qubit group = 64, 0.5, 48.0\n",
+      "Average Creation, Elapsed, Execution Time for the 4 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 4 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 5\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 5 qubit group = 100, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 5 qubit group = 100, 0.5, 75.0\n",
+      "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 5 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 6\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 6 qubit group = 144, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 6 qubit group = 144, 0.5, 108.0\n",
+      "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 6 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 7\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 7 qubit group = 196, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 7 qubit group = 196, 0.5, 147.0\n",
+      "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 7 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 8\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 8 qubit group = 256, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 8 qubit group = 256, 0.5, 192.0\n",
+      "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 8 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 9\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 9 qubit group = 324, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 9 qubit group = 324, 0.5, 243.0\n",
+      "Average Creation, Elapsed, Execution Time for the 9 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 9 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 10\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 10 qubit group = 400, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 10 qubit group = 400, 0.5, 300.0\n",
+      "Average Creation, Elapsed, Execution Time for the 10 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 10 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 11\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 11 qubit group = 484, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 11 qubit group = 484, 0.5, 363.0\n",
+      "Average Creation, Elapsed, Execution Time for the 11 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 11 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 12\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 12 qubit group = 576, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 12 qubit group = 576, 0.5, 432.0\n",
+      "Average Creation, Elapsed, Execution Time for the 12 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 12 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 13\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 13 qubit group = 676, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 13 qubit group = 676, 0.5, 507.0\n",
+      "Average Creation, Elapsed, Execution Time for the 13 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 13 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 14\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 14 qubit group = 784, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 14 qubit group = 784, 0.5, 588.0\n",
+      "Average Creation, Elapsed, Execution Time for the 14 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 14 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 15\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 15 qubit group = 900, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 15 qubit group = 900, 0.5, 675.0\n",
+      "Average Creation, Elapsed, Execution Time for the 15 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 15 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 16\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 16 qubit group = 1024, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 16 qubit group = 1024, 0.5, 768.0\n",
+      "Average Creation, Elapsed, Execution Time for the 16 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 16 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 17\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 17 qubit group = 1156, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 17 qubit group = 1156, 0.5, 867.0\n",
+      "Average Creation, Elapsed, Execution Time for the 17 qubit group = 0.0, 0.005, 0.005 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 17 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 18\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 18 qubit group = 1296, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 18 qubit group = 1296, 0.5, 972.0\n",
+      "Average Creation, Elapsed, Execution Time for the 18 qubit group = 0.0, 0.007, 0.007 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 18 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 19\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 19 qubit group = 1444, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 19 qubit group = 1444, 0.5, 1083.0\n",
+      "Average Creation, Elapsed, Execution Time for the 19 qubit group = 0.0, 0.006, 0.006 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 19 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 20\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 20 qubit group = 1600, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 20 qubit group = 1600, 0.5, 1200.0\n",
+      "Average Creation, Elapsed, Execution Time for the 20 qubit group = 0.0, 0.007, 0.007 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 20 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 21\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 21 qubit group = 1764, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 21 qubit group = 1764, 0.5, 1323.0\n",
+      "Average Creation, Elapsed, Execution Time for the 21 qubit group = 0.0, 0.01, 0.01 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 21 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 22\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 22 qubit group = 1936, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 22 qubit group = 1936, 0.5, 1452.0\n",
+      "Average Creation, Elapsed, Execution Time for the 22 qubit group = 0.0, 0.008, 0.008 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 22 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 23\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 23 qubit group = 2116, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 23 qubit group = 2116, 0.5, 1587.0\n",
+      "Average Creation, Elapsed, Execution Time for the 23 qubit group = 0.0, 0.011, 0.011 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 23 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 24\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 24 qubit group = 2304, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 24 qubit group = 2304, 0.5, 1728.0\n",
+      "Average Creation, Elapsed, Execution Time for the 24 qubit group = 0.0, 0.025, 0.025 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 24 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 25\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 25 qubit group = 2500, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 25 qubit group = 2500, 0.5, 1875.0\n",
+      "Average Creation, Elapsed, Execution Time for the 25 qubit group = 0.0, 0.058, 0.057 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 25 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 26\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 26 qubit group = 2704, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 26 qubit group = 2704, 0.5, 2028.0\n",
+      "Average Creation, Elapsed, Execution Time for the 26 qubit group = 0.0, 0.106, 0.106 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 26 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 27\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 27 qubit group = 2916, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 27 qubit group = 2916, 0.5, 2187.0\n",
+      "Average Creation, Elapsed, Execution Time for the 27 qubit group = 0.0, 0.243, 0.243 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 27 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 28\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 28 qubit group = 3136, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 28 qubit group = 3136, 0.5, 2352.0\n",
+      "Average Creation, Elapsed, Execution Time for the 28 qubit group = 0.0, 0.469, 0.469 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 28 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 29\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 29 qubit group = 3364, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 29 qubit group = 3364, 0.5, 2523.0\n",
+      "Average Creation, Elapsed, Execution Time for the 29 qubit group = 0.0, 0.971, 0.971 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 29 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 30\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 30 qubit group = 3600, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 30 qubit group = 3600, 0.5, 2700.0\n",
+      "Average Creation, Elapsed, Execution Time for the 30 qubit group = 0.0, 1.945, 1.945 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 30 qubit group = 1.0, 1.0\n",
+      "\n",
+      "... execution complete at Sep 29, 2024 05:41:58 UTC in 4.05 secs\n",
+      "\n",
+      "Sample Circuit:\n",
+      "             ╭───╮╭───────────╮     ╭────────────╮                  »\n",
+      "q0 : ──╳───╳─┤ h ├┤ r1(1.571) ├─────┤ r1(0.7854) ├──────────────────»\n",
+      "       │   │ ╰───╯╰─────┬─────╯╭───╮╰─────┬──────╯╭───────────╮     »\n",
+      "q1 : ──╳───╳────────────●──────┤ h ├──────┼───────┤ r1(1.571) ├─────»\n",
+      "                               ╰───╯      │       ╰─────┬─────╯╭───╮»\n",
+      "q2 : ──╳───╳──────────────────────────────●─────────────●──────┤ h ├»\n",
+      "       │   │                                                   ╰───╯»\n",
+      "q3 : ──╳───╳────────────────────────────────────────────────────────»\n",
+      "                                                                    »\n",
+      "q4 : ──────╳───╳────────────────────────────────────────────────────»\n",
+      "     ╭───╮ │   │                                                    »\n",
+      "q5 : ┤ x ├─╳───╳────────────────────────────────────────────────────»\n",
+      "     ╰───╯                                                          »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "╭────────────╮                                ╭────────────╮              »\n",
+      "┤ r1(0.3927) ├────────────────────────────────┤ r1(0.1963) ├──────────────»\n",
+      "╰─────┬──────╯╭────────────╮                  ╰─────┬──────╯╭────────────╮»\n",
+      "──────┼───────┤ r1(0.7854) ├────────────────────────┼───────┤ r1(0.3927) ├»\n",
+      "      │       ╰─────┬──────╯╭───────────╮           │       ╰─────┬──────╯»\n",
+      "──────┼─────────────┼───────┤ r1(1.571) ├───────────┼─────────────┼───────»\n",
+      "      │             │       ╰─────┬─────╯╭───╮      │             │       »\n",
+      "──────●─────────────●─────────────●──────┤ h ├──────┼─────────────┼───────»\n",
+      "                                         ╰───╯      │             │       »\n",
+      "────────────────────────────────────────────────────●─────────────●───────»\n",
+      "                                                                          »\n",
+      "──────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                          »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                                ╭─────────────╮                            »\n",
+      "────────────────────────────────┤ r1(0.09817) ├────────────────────╳───────»\n",
+      "                                ╰──────┬──────╯╭────────────╮      │       »\n",
+      "───────────────────────────────────────┼───────┤ r1(0.1963) ├──────╳───────»\n",
+      "╭────────────╮                         │       ╰─────┬──────╯╭────────────╮»\n",
+      "┤ r1(0.7854) ├─────────────────────────┼─────────────┼───────┤ r1(0.3927) ├»\n",
+      "╰─────┬──────╯╭───────────╮            │             │       ╰─────┬──────╯»\n",
+      "──────┼───────┤ r1(1.571) ├────────────┼─────────────┼─────────────┼───────»\n",
+      "      │       ╰─────┬─────╯╭───╮       │             │             │       »\n",
+      "──────●─────────────●──────┤ h ├───────┼─────────────┼─────────────┼───────»\n",
+      "                           ╰───╯       │             │             │       »\n",
+      "───────────────────────────────────────●─────────────●─────────────●───────»\n",
+      "                                                                           »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "              ╭─────────────╮                                              »\n",
+      "──────╳───────┤ rz(0.09817) ├──╳────────╳──────────────────────────────────»\n",
+      "      │       ├────────────┬╯  │        │                                  »\n",
+      "──────╳───────┤ rz(0.1963) ├───╳────────╳──────────────────────────────────»\n",
+      "              ╰────────────╯      ╭────────────╮                           »\n",
+      "─────────────────────╳─────────╳──┤ rz(0.3927) ├─╳───────╳─────────────────»\n",
+      "╭────────────╮       │         │  ├────────────┤ │       │                 »\n",
+      "┤ r1(0.7854) ├───────╳─────────╳──┤ rz(0.7854) ├─╳───────╳─────────────────»\n",
+      "╰─────┬──────╯ ╭───────────╮      ╰────────────╯   ╭───────────╮           »\n",
+      "──────┼────────┤ r1(1.571) ├────────────╳────────╳─┤ rz(1.571) ├─╳──╳──────»\n",
+      "      │        ╰─────┬─────╯ ╭───╮      │        │ ├───────────┤ │  │ ╭───╮»\n",
+      "──────●──────────────●───────┤ h ├──────╳────────╳─┤ rz(3.142) ├─╳──╳─┤ h ├»\n",
+      "                             ╰───╯                 ╰───────────╯      ╰───╯»\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                                                           ╭──────────────╮»\n",
+      "───────────────────────────────────────────────────────────┤ r1(-0.09817) ├»\n",
+      "                                            ╭─────────────╮╰──────┬───────╯»\n",
+      "────────────────────────────────────────────┤ r1(-0.1963) ├───────┼────────»\n",
+      "                             ╭─────────────╮╰──────┬──────╯       │        »\n",
+      "─────────────────────────────┤ r1(-0.3927) ├───────┼──────────────┼────────»\n",
+      "              ╭─────────────╮╰──────┬──────╯       │              │        »\n",
+      "──────────────┤ r1(-0.7854) ├───────┼──────────────┼──────────────┼────────»\n",
+      "╭────────────╮╰──────┬──────╯       │              │              │        »\n",
+      "┤ r1(-1.571) ├───────┼──────────────┼──────────────┼──────────────┼────────»\n",
+      "╰─────┬──────╯       │              │              │              │        »\n",
+      "──────●──────────────●──────────────●──────────────●──────────────●────────»\n",
+      "                                                                           »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                                                 ╭─────────────╮     »\n",
+      "─────────────────────────────────────────────────┤ r1(-0.1963) ├─────»\n",
+      "                                  ╭─────────────╮╰──────┬──────╯     »\n",
+      "──────────────────────────────────┤ r1(-0.3927) ├───────┼────────────»\n",
+      "                   ╭─────────────╮╰──────┬──────╯       │            »\n",
+      "───────────────────┤ r1(-0.7854) ├───────┼──────────────┼────────────»\n",
+      "     ╭────────────╮╰──────┬──────╯       │              │       ╭───╮»\n",
+      "─────┤ r1(-1.571) ├───────┼──────────────┼──────────────┼───────┤ h ├»\n",
+      "╭───╮╰─────┬──────╯       │              │              │       ╰───╯»\n",
+      "┤ h ├──────●──────────────●──────────────●──────────────●────────────»\n",
+      "╰───╯                                                                »\n",
+      "─────────────────────────────────────────────────────────────────────»\n",
+      "                                                                     »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                             ╭─────────────╮                   ╭─────────────╮»\n",
+      "─────────────────────────────┤ r1(-0.3927) ├───────────────────┤ r1(-0.7854) ├»\n",
+      "              ╭─────────────╮╰──────┬──────╯     ╭────────────╮╰──────┬──────╯»\n",
+      "──────────────┤ r1(-0.7854) ├───────┼────────────┤ r1(-1.571) ├───────┼───────»\n",
+      "╭────────────╮╰──────┬──────╯       │       ╭───╮╰─────┬──────╯       │       »\n",
+      "┤ r1(-1.571) ├───────┼──────────────┼───────┤ h ├──────●──────────────●───────»\n",
+      "╰─────┬──────╯       │              │       ╰───╯                             »\n",
+      "──────●──────────────●──────────────●─────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "     ╭────────────╮╭───╮\n",
+      "─────┤ r1(-1.571) ├┤ h ├\n",
+      "╭───╮╰─────┬──────╯╰───╯\n",
+      "┤ h ├──────●────────────\n",
+      "╰───╯                   \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x1025 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 680x791.818 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import sys\n",
+    "sys.path.insert(1, \"quantum-fourier-transform\")\n",
+    "import qft_benchmark\n",
+    "qft_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
+    "                max_circuits=max_circuits, num_shots=num_shots,\n",
+    "                method=1,\n",
+    "                backend_id=backend_id, provider_backend=provider_backend,\n",
+    "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
+    "                api=api)\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Quantum Phase Estimation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Phase Estimation Benchmark Program - Qiskit\n",
+      "... execution starting at Sep 29, 2024 05:42:00 UTC\n",
+      "... configure execution for target backend_id = nvidia\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 3\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 3 qubit group = 36, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 3 qubit group = 36, 0.5, 27.0\n",
+      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.048, 0.048 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 3 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 4\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 4 qubit group = 64, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 4 qubit group = 64, 0.5, 48.0\n",
+      "Average Creation, Elapsed, Execution Time for the 4 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 4 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 5\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 5 qubit group = 100, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 5 qubit group = 100, 0.5, 75.0\n",
+      "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 5 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 6\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 6 qubit group = 144, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 6 qubit group = 144, 0.5, 108.0\n",
+      "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 6 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 7\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 7 qubit group = 196, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 7 qubit group = 196, 0.5, 147.0\n",
+      "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.003, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 7 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 8\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 8 qubit group = 256, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 8 qubit group = 256, 0.5, 192.0\n",
       "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.002, 0.002 secs\n",
       "Average Hellinger, Normalized Fidelity for the 8 qubit group = 1.0, 1.0\n",
       "\n",
-      "... execution complete at Aug 20, 2024 02:06:30 UTC in 0.074 secs\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 9\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 9 qubit group = 324, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 9 qubit group = 324, 0.5, 243.0\n",
+      "Average Creation, Elapsed, Execution Time for the 9 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 9 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 10\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 10 qubit group = 400, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 10 qubit group = 400, 0.5, 300.0\n",
+      "Average Creation, Elapsed, Execution Time for the 10 qubit group = 0.0, 0.002, 0.002 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 10 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 11\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 11 qubit group = 484, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 11 qubit group = 484, 0.5, 363.0\n",
+      "Average Creation, Elapsed, Execution Time for the 11 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 11 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 12\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 12 qubit group = 576, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 12 qubit group = 576, 0.5, 432.0\n",
+      "Average Creation, Elapsed, Execution Time for the 12 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 12 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 13\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 13 qubit group = 676, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 13 qubit group = 676, 0.5, 507.0\n",
+      "Average Creation, Elapsed, Execution Time for the 13 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 13 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 14\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 14 qubit group = 784, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 14 qubit group = 784, 0.5, 588.0\n",
+      "Average Creation, Elapsed, Execution Time for the 14 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 14 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 15\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 15 qubit group = 900, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 15 qubit group = 900, 0.5, 675.0\n",
+      "Average Creation, Elapsed, Execution Time for the 15 qubit group = 0.0, 0.003, 0.003 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 15 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 16\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 16 qubit group = 1024, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 16 qubit group = 1024, 0.5, 768.0\n",
+      "Average Creation, Elapsed, Execution Time for the 16 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 16 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 17\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 17 qubit group = 1156, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 17 qubit group = 1156, 0.5, 867.0\n",
+      "Average Creation, Elapsed, Execution Time for the 17 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 17 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 18\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 18 qubit group = 1296, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 18 qubit group = 1296, 0.5, 972.0\n",
+      "Average Creation, Elapsed, Execution Time for the 18 qubit group = 0.0, 0.006, 0.006 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 18 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 19\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 19 qubit group = 1444, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 19 qubit group = 1444, 0.5, 1083.0\n",
+      "Average Creation, Elapsed, Execution Time for the 19 qubit group = 0.0, 0.005, 0.005 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 19 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 20\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 20 qubit group = 1600, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 20 qubit group = 1600, 0.5, 1200.0\n",
+      "Average Creation, Elapsed, Execution Time for the 20 qubit group = 0.0, 0.006, 0.006 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 20 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 21\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 21 qubit group = 1764, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 21 qubit group = 1764, 0.5, 1323.0\n",
+      "Average Creation, Elapsed, Execution Time for the 21 qubit group = 0.0, 0.007, 0.006 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 21 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 22\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 22 qubit group = 1936, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 22 qubit group = 1936, 0.5, 1452.0\n",
+      "Average Creation, Elapsed, Execution Time for the 22 qubit group = 0.0, 0.008, 0.008 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 22 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 23\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 23 qubit group = 2116, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 23 qubit group = 2116, 0.5, 1587.0\n",
+      "Average Creation, Elapsed, Execution Time for the 23 qubit group = 0.0, 0.012, 0.012 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 23 qubit group = 0.993, 0.993\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 24\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 24 qubit group = 2304, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 24 qubit group = 2304, 0.5, 1728.0\n",
+      "Average Creation, Elapsed, Execution Time for the 24 qubit group = 0.0, 0.025, 0.025 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 24 qubit group = 0.997, 0.997\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 25\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 25 qubit group = 2500, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 25 qubit group = 2500, 0.5, 1875.0\n",
+      "Average Creation, Elapsed, Execution Time for the 25 qubit group = 0.0, 0.049, 0.049 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 25 qubit group = 0.994, 0.994\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 26\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 26 qubit group = 2704, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 26 qubit group = 2704, 0.5, 2028.0\n",
+      "Average Creation, Elapsed, Execution Time for the 26 qubit group = 0.0, 0.097, 0.097 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 26 qubit group = 1.0, 1.0\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 27\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 27 qubit group = 2916, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 27 qubit group = 2916, 0.5, 2187.0\n",
+      "Average Creation, Elapsed, Execution Time for the 27 qubit group = 0.0, 0.18, 0.18 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 27 qubit group = 0.995, 0.995\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 28\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 28 qubit group = 3136, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 28 qubit group = 3136, 0.5, 2352.0\n",
+      "Average Creation, Elapsed, Execution Time for the 28 qubit group = 0.0, 0.35, 0.35 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 28 qubit group = 0.997, 0.997\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 29\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 29 qubit group = 3364, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 29 qubit group = 3364, 0.5, 2523.0\n",
+      "Average Creation, Elapsed, Execution Time for the 29 qubit group = 0.0, 0.701, 0.701 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 29 qubit group = 0.256, 0.256\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 30\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 30 qubit group = 3600, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 30 qubit group = 3600, 0.5, 2700.0\n",
+      "Average Creation, Elapsed, Execution Time for the 30 qubit group = 0.0, 1.392, 1.392 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 30 qubit group = 0.001, 0.001\n",
+      "\n",
+      "... execution complete at Sep 29, 2024 05:42:03 UTC in 3.074 secs\n",
       "\n",
       "Sample Circuit:\n",
-      "     ╭───╮                         ╭───╮\n",
-      "q0 : ┤ h ├──────────────────────●──┤ h ├\n",
-      "     ├───┤                      │  ├───┤\n",
-      "q1 : ┤ h ├──────────────────────┼──┤ h ├\n",
-      "     ├───┤                      │  ├───┤\n",
-      "q2 : ┤ h ├─────────────────●────┼──┤ h ├\n",
-      "     ├───┤                 │    │  ├───┤\n",
-      "q3 : ┤ h ├────────────●────┼────┼──┤ h ├\n",
-      "     ├───┤            │    │    │  ├───┤\n",
-      "q4 : ┤ h ├────────────┼────┼────┼──┤ h ├\n",
-      "     ├───┤            │    │    │  ├───┤\n",
-      "q5 : ┤ h ├────────────┼────┼────┼──┤ h ├\n",
-      "     ├───┤            │    │    │  ├───┤\n",
-      "q6 : ┤ h ├───────●────┼────┼────┼──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╭─┴─╮╭─┴─╮╭─┴─╮╰───╯\n",
-      "q7 : ┤ h ├┤ z ├┤ x ├┤ x ├┤ x ├┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯╰───╯╰───╯╰───╯     \n",
+      "     ╭───╮                                                                    »\n",
+      "q0 : ┤ h ├──────●─────────────────────────────────────────────────────────────»\n",
+      "     ├───┤      │                                                             »\n",
+      "q1 : ┤ h ├──────┼─────────────●───────────────────────────────────────────────»\n",
+      "     ├───┤      │             │                                               »\n",
+      "q2 : ┤ h ├──────┼─────────────┼─────────────●─────────────────────────────────»\n",
+      "     ├───┤      │             │             │                                 »\n",
+      "q3 : ┤ h ├──────┼─────────────┼─────────────┼─────────────●───────────────────»\n",
+      "     ├───┤      │             │             │             │                   »\n",
+      "q4 : ┤ h ├──────┼─────────────┼─────────────┼─────────────┼────────────●──────»\n",
+      "     ├───┤╭─────┴──────╮╭─────┴──────╮╭─────┴──────╮╭─────┴─────╮╭─────┴─────╮»\n",
+      "q5 : ┤ x ├┤ r1(0.1963) ├┤ r1(0.3927) ├┤ r1(0.7854) ├┤ r1(1.571) ├┤ r1(3.142) ├»\n",
+      "     ╰───╯╰────────────╯╰────────────╯╰────────────╯╰───────────╯╰───────────╯»\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                                                          ╭─────────────╮     »\n",
+      "─╳──╳─────────────────────────────────────────────────────┤ r1(-0.1963) ├─────»\n",
+      " │  │                                      ╭─────────────╮╰──────┬──────╯     »\n",
+      "─┼──┼───╳───╳──────────────────────────────┤ r1(-0.3927) ├───────┼────────────»\n",
+      " │  │   │   │               ╭─────────────╮╰──────┬──────╯       │            »\n",
+      "─┼──┼───┼───┼───────────────┤ r1(-0.7854) ├───────┼──────────────┼────────────»\n",
+      " │  │   │   │ ╭────────────╮╰──────┬──────╯       │              │       ╭───╮»\n",
+      "─┼──┼───╳───╳─┤ r1(-1.571) ├───────┼──────────────┼──────────────┼───────┤ h ├»\n",
+      " │  │ ╭───╮   ╰─────┬──────╯       │              │              │       ╰───╯»\n",
+      "─╳──╳─┤ h ├─────────●──────────────●──────────────●──────────────●────────────»\n",
+      "      ╰───╯                                                                   »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                             ╭─────────────╮                   ╭─────────────╮»\n",
+      "─────────────────────────────┤ r1(-0.3927) ├───────────────────┤ r1(-0.7854) ├»\n",
+      "              ╭─────────────╮╰──────┬──────╯     ╭────────────╮╰──────┬──────╯»\n",
+      "──────────────┤ r1(-0.7854) ├───────┼────────────┤ r1(-1.571) ├───────┼───────»\n",
+      "╭────────────╮╰──────┬──────╯       │       ╭───╮╰─────┬──────╯       │       »\n",
+      "┤ r1(-1.571) ├───────┼──────────────┼───────┤ h ├──────●──────────────●───────»\n",
+      "╰─────┬──────╯       │              │       ╰───╯                             »\n",
+      "──────●──────────────●──────────────●─────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "     ╭────────────╮╭───╮\n",
+      "─────┤ r1(-1.571) ├┤ h ├\n",
+      "╭───╮╰─────┬──────╯╰───╯\n",
+      "┤ h ├──────●────────────\n",
+      "╰───╯                   \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 600x800 with 3 Axes>"
+       "<Figure size 600x1025 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -122,9 +1082,9 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 680x451.818 with 2 Axes>"
+       "<Figure size 680x791.818 with 2 Axes>"
       ]
      },
      "metadata": {},
@@ -133,12 +1093,10 @@
    ],
    "source": [
     "import sys\n",
-    "sys.path.insert(1, \"bernstein-vazirani\")\n",
-    "sys.path.insert(1, \"bernstein-vazirani/cudaq\")\n",
-    "import bv_benchmark\n",
-    "bv_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
+    "sys.path.insert(1, \"phase-estimation\")\n",
+    "import pe_benchmark\n",
+    "pe_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
     "                max_circuits=max_circuits, num_shots=num_shots,\n",
-    "                method=1,\n",
     "                backend_id=backend_id, provider_backend=provider_backend,\n",
     "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
     "                api=api)"
@@ -148,74 +1106,141 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Bernstein-Vazirani - Method 2"
+    "# Execute Benchmarks with Noise\n",
+    "The next set of cells configures the benchmarks to execute on the density-matrix-cpu simulator where noise is simulated during execution.\n",
+    "Noisy simulation is costly and hence we limit the number of qubits executed to only 10 here.\n",
+    "The default noise model used sets a depolarization channel with a value 0.04 on all single-qubit operations."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {
-    "tags": []
-   },
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Specify the parameters for the benchmark\n",
+    "min_qubits=2\n",
+    "max_qubits=8                        # execute fewer qubits with noise model\n",
+    "skip_qubits=1\n",
+    "max_circuits=1\n",
+    "num_shots=1000\n",
+    "\n",
+    "# Set these default parameters\n",
+    "hub=\"\"; group=\"\"; project=\"\"\n",
+    "provider_backend = None\n",
+    "exec_options = {}\n",
+    "\n",
+    "# For CUDA Quantum, specify this API option\n",
+    "api=\"cudaq\"\n",
+    "\n",
+    "# For noisy simulation, use this target\n",
+    "backend_id=\"density-matrix-cpu\"\n",
+    "\n",
+    "# OPTIONS: updated for running with noise\n",
+    "import sys\n",
+    "sys.path.insert(1, \"_common\")\n",
+    "sys.path.insert(1, \"_common/cudaq\")\n",
+    "\n",
+    "# Uncomment these lines to add a suffix to your backend_id and data/image files\n",
+    "import metrics\n",
+    "metrics.data_suffix = \"-mysystem-noisy\"\n",
+    "\n",
+    "# Uncomment these lines to add a default noise model for execution\n",
+    "import execute\n",
+    "execute.set_default_noise_model()                # for simple default noise model\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Bernstein-Vazirani - Method 1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Bernstein-Vazirani (2) Benchmark Program - Qiskit\n",
-      "... execution starting at Aug 20, 2024 02:06:32 UTC\n",
+      "Bernstein-Vazirani (1) Benchmark Program - Qiskit\n",
+      "... execution starting at Sep 29, 2024 05:42:05 UTC\n",
+      "... configure execution for target backend_id = density-matrix-cpu\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 3\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.003, 0.003 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 3 qubit group = 0.0, 0.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 3 qubit group = 36, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 3 qubit group = 36, 0.5, 27.0\n",
+      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.053, 0.053 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 3 qubit group = 0.851, 0.801\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 4\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 4 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 4 qubit group = 1.0, 1.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 4 qubit group = 64, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 4 qubit group = 64, 0.5, 48.0\n",
+      "Average Creation, Elapsed, Execution Time for the 4 qubit group = 0.0, 0.001, 0.001 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 4 qubit group = 0.801, 0.773\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 5\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 5 qubit group = 0.0, 0.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 5 qubit group = 100, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 5 qubit group = 100, 0.5, 75.0\n",
+      "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.004, 0.004 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 5 qubit group = 0.758, 0.742\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 6\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 6 qubit group = 0.0, 0.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 6 qubit group = 144, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 6 qubit group = 144, 0.5, 108.0\n",
+      "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.022, 0.022 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 6 qubit group = 0.729, 0.72\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 7\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 7 qubit group = 0.0, 0.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 7 qubit group = 196, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 7 qubit group = 196, 0.5, 147.0\n",
+      "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.105, 0.105 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 7 qubit group = 0.683, 0.678\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 8\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 8 qubit group = 0.0, 0.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 8 qubit group = 256, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 8 qubit group = 256, 0.5, 192.0\n",
+      "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.394, 0.394 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 8 qubit group = 0.659, 0.656\n",
       "\n",
-      "... execution complete at Aug 20, 2024 02:06:32 UTC in 0.016 secs\n",
+      "... execution complete at Sep 29, 2024 05:42:05 UTC in 0.583 secs\n",
       "\n",
       "Sample Circuit:\n",
-      "     ╭───╮          ╭───╮╭───╮     ╭───╮\n",
-      "q0 : ┤ h ├───────●──┤ h ├┤ h ├──●──┤ h ├\n",
-      "     ├───┤╭───╮╭─┴─╮╰───╯╰───╯╭─┴─╮╰───╯\n",
-      "q1 : ┤ x ├┤ h ├┤ x ├──────────┤ x ├─────\n",
-      "     ╰───╯╰───╯╰───╯          ╰───╯     \n",
+      "     ╭───╮╭───╮          \n",
+      "q0 : ┤ h ├┤ h ├──────────\n",
+      "     ├───┤╰───╯     ╭───╮\n",
+      "q1 : ┤ h ├───────●──┤ h ├\n",
+      "     ├───┤       │  ├───┤\n",
+      "q2 : ┤ h ├───────┼──┤ h ├\n",
+      "     ├───┤       │  ├───┤\n",
+      "q3 : ┤ h ├───────┼──┤ h ├\n",
+      "     ├───┤       │  ├───┤\n",
+      "q4 : ┤ h ├───────┼──┤ h ├\n",
+      "     ├───┤╭───╮╭─┴─╮╰───╯\n",
+      "q5 : ┤ h ├┤ z ├┤ x ├─────\n",
+      "     ╰───╯╰───╯╰───╯     \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 600x800 with 3 Axes>"
+       "<Figure size 600x1025 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -223,7 +1248,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 680x451.818 with 2 Axes>"
       ]
@@ -235,11 +1260,10 @@
    "source": [
     "import sys\n",
     "sys.path.insert(1, \"bernstein-vazirani\")\n",
-    "sys.path.insert(1, \"bernstein-vazirani/cudaq\")\n",
     "import bv_benchmark\n",
     "bv_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
     "                max_circuits=max_circuits, num_shots=num_shots,\n",
-    "                method=2,\n",
+    "                method=1,\n",
     "                backend_id=backend_id, provider_backend=provider_backend,\n",
     "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
     "                api=api)"
@@ -254,187 +1278,138 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Not implemented yet\\nimport qft_benchmark\\nqft_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\\n                max_circuits=max_circuits, num_shots=num_shots,\\n                method=1,\\n                backend_id=backend_id, provider_backend=provider_backend,\\n                hub=hub, group=group, project=project, exec_options=exec_options,\\n                api=api)\\n'"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.path.insert(1, \"quantum-fourier-transform\")\n",
-    "sys.path.insert(1, \"quantum-fourier-transform/cudaq\")\n",
-    "\"\"\" Not implemented yet\n",
-    "import qft_benchmark\n",
-    "qft_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
-    "                max_circuits=max_circuits, num_shots=num_shots,\n",
-    "                method=1,\n",
-    "                backend_id=backend_id, provider_backend=provider_backend,\n",
-    "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
-    "                api=api)\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Quantum Fourier Transform - Method 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "' Not implemented yet\\nimport qft_benchmark\\nqft_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\\n                max_circuits=max_circuits, num_shots=num_shots,\\n                method=2,\\n                backend_id=backend_id, provider_backend=provider_backend,\\n                hub=hub, group=group, project=project, exec_options=exec_options,\\n                api=api)\\n'"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import sys\n",
-    "sys.path.insert(1, \"quantum-fourier-transform\")\n",
-    "sys.path.insert(1, \"quantum-fourier-transform/cudaq\")\n",
-    "\"\"\" Not implemented yet\n",
-    "import qft_benchmark\n",
-    "qft_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
-    "                max_circuits=max_circuits, num_shots=num_shots,\n",
-    "                method=2,\n",
-    "                backend_id=backend_id, provider_backend=provider_backend,\n",
-    "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
-    "                api=api)\n",
-    "\"\"\""
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Phase Estimation"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
+   "execution_count": 7,
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Phase Estimation Benchmark Program - Qiskit\n",
-      "... execution starting at Aug 20, 2024 02:06:33 UTC\n",
+      "Quantum-Fourier-Transform (1) Benchmark Program - Qiskit\n",
+      "... execution starting at Sep 29, 2024 05:42:07 UTC\n",
+      "... configure execution for target backend_id = density-matrix-cpu\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 2\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 2 qubit group = 16, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 2 qubit group = 16, 0.5, 12.0\n",
+      "Average Creation, Elapsed, Execution Time for the 2 qubit group = 0.0, 0.007, 0.007 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 2 qubit group = 0.805, 0.74\n",
+      "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 3\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.045, 0.045 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 3 qubit group = 1.0, 1.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 3 qubit group = 36, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 3 qubit group = 36, 0.5, 27.0\n",
+      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.011, 0.011 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 3 qubit group = 0.746, 0.71\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 4\n",
       "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 4 qubit group = 64, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 4 qubit group = 64, 0.5, 48.0\n",
       "Average Creation, Elapsed, Execution Time for the 4 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 4 qubit group = 1.0, 1.0\n",
+      "Average Hellinger, Normalized Fidelity for the 4 qubit group = 0.698, 0.678\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 5\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 5 qubit group = 1.0, 1.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 5 qubit group = 100, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 5 qubit group = 100, 0.5, 75.0\n",
+      "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.035, 0.035 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 5 qubit group = 0.636, 0.624\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 6\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 6 qubit group = 1.0, 1.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 6 qubit group = 144, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 6 qubit group = 144, 0.5, 108.0\n",
+      "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.035, 0.035 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 6 qubit group = 0.609, 0.603\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 7\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 7 qubit group = 1.0, 1.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 7 qubit group = 196, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 7 qubit group = 196, 0.5, 147.0\n",
+      "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.215, 0.215 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 7 qubit group = 0.51, 0.506\n",
       "\n",
       "************\n",
       "Executing [1] circuits with num_qubits = 8\n",
       "************\n",
-      "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.002, 0.002 secs\n",
-      "Average Hellinger, Normalized Fidelity for the 8 qubit group = 1.0, 1.0\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 8 qubit group = 256, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 8 qubit group = 256, 0.5, 192.0\n",
+      "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.725, 0.725 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 8 qubit group = 0.497, 0.495\n",
       "\n",
-      "... execution complete at Aug 20, 2024 02:06:33 UTC in 0.058 secs\n",
+      "... execution complete at Sep 29, 2024 05:42:08 UTC in 1.037 secs\n",
       "\n",
       "Sample Circuit:\n",
-      "     ╭───╮                                                                 »\n",
-      "q0 : ┤ h ├──────●──────────────────────────────────────────────────────────»\n",
-      "     ├───┤      │                                                          »\n",
-      "q1 : ┤ h ├──────┼────────────●─────────────────────────────────────────────»\n",
-      "     ├───┤      │            │                                             »\n",
-      "q2 : ┤ h ├──────┼────────────┼────────────●────────────────────────────────»\n",
-      "     ├───┤      │            │            │                                »\n",
-      "q3 : ┤ h ├──────┼────────────┼────────────┼────────────●───────────────────»\n",
-      "     ├───┤      │            │            │            │                   »\n",
-      "q4 : ┤ h ├──────┼────────────┼────────────┼────────────┼────────────●──────»\n",
-      "     ├───┤      │            │            │            │            │      »\n",
-      "q5 : ┤ h ├──────┼────────────┼────────────┼────────────┼────────────┼──────»\n",
-      "     ├───┤      │            │            │            │            │      »\n",
-      "q6 : ┤ h ├──────┼────────────┼────────────┼────────────┼────────────┼──────»\n",
-      "     ├───┤╭─────┴─────╮╭─────┴─────╮╭─────┴─────╮╭─────┴─────╮╭─────┴─────╮»\n",
-      "q7 : ┤ x ├┤ r1(3.191) ├┤ r1(6.381) ├┤ r1(12.76) ├┤ r1(25.53) ├┤ r1(51.05) ├»\n",
-      "     ╰───╯╰───────────╯╰───────────╯╰───────────╯╰───────────╯╰───────────╯»\n",
+      "             ╭───╮╭───────────╮     ╭────────────╮                  »\n",
+      "q0 : ──╳───╳─┤ h ├┤ r1(1.571) ├─────┤ r1(0.7854) ├──────────────────»\n",
+      "       │   │ ╰───╯╰─────┬─────╯╭───╮╰─────┬──────╯╭───────────╮     »\n",
+      "q1 : ──╳───╳────────────●──────┤ h ├──────┼───────┤ r1(1.571) ├─────»\n",
+      "                               ╰───╯      │       ╰─────┬─────╯╭───╮»\n",
+      "q2 : ──╳───╳──────────────────────────────●─────────────●──────┤ h ├»\n",
+      "       │   │                                                   ╰───╯»\n",
+      "q3 : ──╳───╳────────────────────────────────────────────────────────»\n",
+      "                                                                    »\n",
+      "q4 : ──────╳───╳────────────────────────────────────────────────────»\n",
+      "     ╭───╮ │   │                                                    »\n",
+      "q5 : ┤ x ├─╳───╳────────────────────────────────────────────────────»\n",
+      "     ╰───╯                                                          »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "╭────────────╮                                ╭────────────╮              »\n",
+      "┤ r1(0.3927) ├────────────────────────────────┤ r1(0.1963) ├──────────────»\n",
+      "╰─────┬──────╯╭────────────╮                  ╰─────┬──────╯╭────────────╮»\n",
+      "──────┼───────┤ r1(0.7854) ├────────────────────────┼───────┤ r1(0.3927) ├»\n",
+      "      │       ╰─────┬──────╯╭───────────╮           │       ╰─────┬──────╯»\n",
+      "──────┼─────────────┼───────┤ r1(1.571) ├───────────┼─────────────┼───────»\n",
+      "      │             │       ╰─────┬─────╯╭───╮      │             │       »\n",
+      "──────●─────────────●─────────────●──────┤ h ├──────┼─────────────┼───────»\n",
+      "                                         ╰───╯      │             │       »\n",
+      "────────────────────────────────────────────────────●─────────────●───────»\n",
+      "                                                                          »\n",
+      "──────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                          »\n",
       "\n",
       "################################################################################\n",
       "\n",
-      "                                                                        »\n",
-      "───────────────────────────╳──╳─────────────────────────────────────────»\n",
-      "                           │  │                                         »\n",
-      "───────────────────────────┼──┼───╳───╳─────────────────────────────────»\n",
-      "                           │  │   │   │                                 »\n",
-      "───────────────────────────┼──┼───┼───┼───────╳────────╳────────────────»\n",
-      "                           │  │   │   │       │        │                »\n",
-      "───────────────────────────┼──┼───┼───┼───────┼────────┼────────────────»\n",
-      "                           │  │   │   │       │        │ ╭─────────────╮»\n",
-      "───────────────────────────┼──┼───┼───┼───────╳────────╳─┤ r1(-0.7854) ├»\n",
-      "                           │  │   │   │ ╭────────────╮   ╰──────┬──────╯»\n",
-      "──────●────────────────────┼──┼───╳───╳─┤ r1(-1.571) ├──────────┼───────»\n",
-      "      │                    │  │ ╭───╮   ╰─────┬──────╯          │       »\n",
-      "──────┼────────────●───────╳──╳─┤ h ├─────────●─────────────────●───────»\n",
-      "╭─────┴─────╮╭─────┴─────╮      ╰───╯                                   »\n",
-      "┤ r1(102.1) ├┤ r1(204.2) ├──────────────────────────────────────────────»\n",
-      "╰───────────╯╰───────────╯                                              »\n",
+      "                                ╭─────────────╮                            »\n",
+      "────────────────────────────────┤ r1(0.09817) ├────────────────────╳───────»\n",
+      "                                ╰──────┬──────╯╭────────────╮      │       »\n",
+      "───────────────────────────────────────┼───────┤ r1(0.1963) ├──────╳───────»\n",
+      "╭────────────╮                         │       ╰─────┬──────╯╭────────────╮»\n",
+      "┤ r1(0.7854) ├─────────────────────────┼─────────────┼───────┤ r1(0.3927) ├»\n",
+      "╰─────┬──────╯╭───────────╮            │             │       ╰─────┬──────╯»\n",
+      "──────┼───────┤ r1(1.571) ├────────────┼─────────────┼─────────────┼───────»\n",
+      "      │       ╰─────┬─────╯╭───╮       │             │             │       »\n",
+      "──────●─────────────●──────┤ h ├───────┼─────────────┼─────────────┼───────»\n",
+      "                           ╰───╯       │             │             │       »\n",
+      "───────────────────────────────────────●─────────────●─────────────●───────»\n",
+      "                                                                           »\n",
       "\n",
       "################################################################################\n",
       "\n",
-      "                                              ╭──────────────╮     »\n",
-      "──────────────────────────────────────────────┤ r1(-0.04909) ├─────»\n",
-      "                              ╭──────────────╮╰──────┬───────╯     »\n",
-      "──────────────────────────────┤ r1(-0.09817) ├───────┼─────────────»\n",
-      "               ╭─────────────╮╰──────┬───────╯       │             »\n",
-      "───────────────┤ r1(-0.1963) ├───────┼───────────────┼─────────────»\n",
-      "╭─────────────╮╰──────┬──────╯       │               │             »\n",
-      "┤ r1(-0.3927) ├───────┼──────────────┼───────────────┼─────────────»\n",
-      "╰──────┬──────╯       │              │               │             »\n",
-      "───────┼──────────────┼──────────────┼───────────────┼─────────────»\n",
-      "       │              │              │               │        ╭───╮»\n",
-      "───────┼──────────────┼──────────────┼───────────────┼────────┤ h ├»\n",
-      "       │              │              │               │        ╰───╯»\n",
-      "───────●──────────────●──────────────●───────────────●─────────────»\n",
-      "                                                                   »\n",
-      "───────────────────────────────────────────────────────────────────»\n",
-      "                                                                   »\n",
+      "              ╭─────────────╮                                              »\n",
+      "──────╳───────┤ rz(0.09817) ├──╳────────╳──────────────────────────────────»\n",
+      "      │       ├────────────┬╯  │        │                                  »\n",
+      "──────╳───────┤ rz(0.1963) ├───╳────────╳──────────────────────────────────»\n",
+      "              ╰────────────╯      ╭────────────╮                           »\n",
+      "─────────────────────╳─────────╳──┤ rz(0.3927) ├─╳───────╳─────────────────»\n",
+      "╭────────────╮       │         │  ├────────────┤ │       │                 »\n",
+      "┤ r1(0.7854) ├───────╳─────────╳──┤ rz(0.7854) ├─╳───────╳─────────────────»\n",
+      "╰─────┬──────╯ ╭───────────╮      ╰────────────╯   ╭───────────╮           »\n",
+      "──────┼────────┤ r1(1.571) ├────────────╳────────╳─┤ rz(1.571) ├─╳──╳──────»\n",
+      "      │        ╰─────┬─────╯ ╭───╮      │        │ ├───────────┤ │  │ ╭───╮»\n",
+      "──────●──────────────●───────┤ h ├──────╳────────╳─┤ rz(3.142) ├─╳──╳─┤ h ├»\n",
+      "                             ╰───╯                 ╰───────────╯      ╰───╯»\n",
       "\n",
       "################################################################################\n",
       "\n",
@@ -451,10 +1426,6 @@
       "╰─────┬──────╯       │              │              │              │        »\n",
       "──────●──────────────●──────────────●──────────────●──────────────●────────»\n",
       "                                                                           »\n",
-      "───────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                           »\n",
-      "───────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                           »\n",
       "\n",
       "################################################################################\n",
       "\n",
@@ -471,10 +1442,6 @@
       "╰───╯                                                                »\n",
       "─────────────────────────────────────────────────────────────────────»\n",
       "                                                                     »\n",
-      "─────────────────────────────────────────────────────────────────────»\n",
-      "                                                                     »\n",
-      "─────────────────────────────────────────────────────────────────────»\n",
-      "                                                                     »\n",
       "\n",
       "################################################################################\n",
       "\n",
@@ -491,10 +1458,6 @@
       "                                                                              »\n",
       "──────────────────────────────────────────────────────────────────────────────»\n",
       "                                                                              »\n",
-      "──────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                              »\n",
-      "──────────────────────────────────────────────────────────────────────────────»\n",
-      "                                                                              »\n",
       "\n",
       "################################################################################\n",
       "\n",
@@ -511,18 +1474,14 @@
       "                        \n",
       "────────────────────────\n",
       "                        \n",
-      "────────────────────────\n",
-      "                        \n",
-      "────────────────────────\n",
-      "                        \n",
       "\n"
      ]
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 600x800 with 3 Axes>"
+       "<Figure size 600x1025 with 4 Axes>"
       ]
      },
      "metadata": {},
@@ -530,7 +1489,7 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 680x451.818 with 2 Axes>"
       ]
@@ -541,31 +1500,163 @@
    ],
    "source": [
     "import sys\n",
-    "sys.path.insert(1, \"phase-estimation\")\n",
-    "sys.path.insert(1, \"phase-estimation/cudaq\")\n",
-    "import pe_benchmark\n",
-    "pe_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
+    "sys.path.insert(1, \"quantum-fourier-transform\")\n",
+    "import qft_benchmark\n",
+    "qft_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
     "                max_circuits=max_circuits, num_shots=num_shots,\n",
+    "                method=1,\n",
     "                backend_id=backend_id, provider_backend=provider_backend,\n",
     "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
-    "                api=api)"
+    "                api=api)\n"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Combined Benchmark Results"
+    "### Quantum Phase Estimation"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Phase Estimation Benchmark Program - Qiskit\n",
+      "... execution starting at Sep 29, 2024 05:42:09 UTC\n",
+      "... configure execution for target backend_id = density-matrix-cpu\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 3\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 3 qubit group = 36, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 3 qubit group = 36, 0.5, 27.0\n",
+      "Average Creation, Elapsed, Execution Time for the 3 qubit group = 0.0, 0.012, 0.012 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 3 qubit group = 0.857, 0.809\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 4\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 4 qubit group = 64, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 4 qubit group = 64, 0.5, 48.0\n",
+      "Average Creation, Elapsed, Execution Time for the 4 qubit group = 0.0, 0.001, 0.001 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 4 qubit group = 0.799, 0.77\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 5\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 5 qubit group = 100, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 5 qubit group = 100, 0.5, 75.0\n",
+      "Average Creation, Elapsed, Execution Time for the 5 qubit group = 0.0, 0.006, 0.006 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 5 qubit group = 0.755, 0.739\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 6\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 6 qubit group = 144, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 6 qubit group = 144, 0.5, 108.0\n",
+      "Average Creation, Elapsed, Execution Time for the 6 qubit group = 0.0, 0.022, 0.022 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 6 qubit group = 0.769, 0.762\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 7\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 7 qubit group = 196, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 7 qubit group = 196, 0.5, 147.0\n",
+      "Average Creation, Elapsed, Execution Time for the 7 qubit group = 0.0, 0.106, 0.106 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 7 qubit group = 0.709, 0.704\n",
+      "\n",
+      "************\n",
+      "Executing [1] circuits with num_qubits = 8\n",
+      "************\n",
+      "Average Circuit Algorithmic Depth, ξ (xi) for the 8 qubit group = 256, 0.5\n",
+      "Average Normalized Transpiled Depth, ξ (xi), 2q gates for the 8 qubit group = 256, 0.5, 192.0\n",
+      "Average Creation, Elapsed, Execution Time for the 8 qubit group = 0.0, 0.379, 0.379 secs\n",
+      "Average Hellinger, Normalized Fidelity for the 8 qubit group = 0.648, 0.645\n",
+      "\n",
+      "... execution complete at Sep 29, 2024 05:42:09 UTC in 0.533 secs\n",
+      "\n",
+      "Sample Circuit:\n",
+      "     ╭───╮                                                                    »\n",
+      "q0 : ┤ h ├──────●─────────────────────────────────────────────────────────────»\n",
+      "     ├───┤      │                                                             »\n",
+      "q1 : ┤ h ├──────┼─────────────●───────────────────────────────────────────────»\n",
+      "     ├───┤      │             │                                               »\n",
+      "q2 : ┤ h ├──────┼─────────────┼─────────────●─────────────────────────────────»\n",
+      "     ├───┤      │             │             │                                 »\n",
+      "q3 : ┤ h ├──────┼─────────────┼─────────────┼─────────────●───────────────────»\n",
+      "     ├───┤      │             │             │             │                   »\n",
+      "q4 : ┤ h ├──────┼─────────────┼─────────────┼─────────────┼────────────●──────»\n",
+      "     ├───┤╭─────┴──────╮╭─────┴──────╮╭─────┴──────╮╭─────┴─────╮╭─────┴─────╮»\n",
+      "q5 : ┤ x ├┤ r1(0.1963) ├┤ r1(0.3927) ├┤ r1(0.7854) ├┤ r1(1.571) ├┤ r1(3.142) ├»\n",
+      "     ╰───╯╰────────────╯╰────────────╯╰────────────╯╰───────────╯╰───────────╯»\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                                                          ╭─────────────╮     »\n",
+      "─╳──╳─────────────────────────────────────────────────────┤ r1(-0.1963) ├─────»\n",
+      " │  │                                      ╭─────────────╮╰──────┬──────╯     »\n",
+      "─┼──┼───╳───╳──────────────────────────────┤ r1(-0.3927) ├───────┼────────────»\n",
+      " │  │   │   │               ╭─────────────╮╰──────┬──────╯       │            »\n",
+      "─┼──┼───┼───┼───────────────┤ r1(-0.7854) ├───────┼──────────────┼────────────»\n",
+      " │  │   │   │ ╭────────────╮╰──────┬──────╯       │              │       ╭───╮»\n",
+      "─┼──┼───╳───╳─┤ r1(-1.571) ├───────┼──────────────┼──────────────┼───────┤ h ├»\n",
+      " │  │ ╭───╮   ╰─────┬──────╯       │              │              │       ╰───╯»\n",
+      "─╳──╳─┤ h ├─────────●──────────────●──────────────●──────────────●────────────»\n",
+      "      ╰───╯                                                                   »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "                             ╭─────────────╮                   ╭─────────────╮»\n",
+      "─────────────────────────────┤ r1(-0.3927) ├───────────────────┤ r1(-0.7854) ├»\n",
+      "              ╭─────────────╮╰──────┬──────╯     ╭────────────╮╰──────┬──────╯»\n",
+      "──────────────┤ r1(-0.7854) ├───────┼────────────┤ r1(-1.571) ├───────┼───────»\n",
+      "╭────────────╮╰──────┬──────╯       │       ╭───╮╰─────┬──────╯       │       »\n",
+      "┤ r1(-1.571) ├───────┼──────────────┼───────┤ h ├──────●──────────────●───────»\n",
+      "╰─────┬──────╯       │              │       ╰───╯                             »\n",
+      "──────●──────────────●──────────────●─────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "──────────────────────────────────────────────────────────────────────────────»\n",
+      "                                                                              »\n",
+      "\n",
+      "################################################################################\n",
+      "\n",
+      "     ╭────────────╮╭───╮\n",
+      "─────┤ r1(-1.571) ├┤ h ├\n",
+      "╭───╮╰─────┬──────╯╰───╯\n",
+      "┤ h ├──────●────────────\n",
+      "╰───╯                   \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "────────────────────────\n",
+      "                        \n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x1025 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 680x451.818 with 2 Axes>"
       ]
@@ -576,16 +1667,13 @@
    ],
    "source": [
     "import sys\n",
-    "sys.path.insert(1, \"_common\")\n",
-    "import metrics\n",
-    "\n",
-    "metrics.aq_mode = 0\n",
-    "# metrics.depth_base = 2\n",
-    "# metrics.QV = 0\n",
-    "# apps = [ \"Hidden Shift\", \"Grover's Search\", \"Quantum Fourier Transform (1)\", \"Hamiltonian Simulation\" ]\n",
-    "# backend_id='qasm_simulator'\n",
-    "\n",
-    "metrics.plot_all_app_metrics(backend_id, do_all_plots=False, include_apps=None)"
+    "sys.path.insert(1, \"phase-estimation\")\n",
+    "import pe_benchmark\n",
+    "pe_benchmark.run(min_qubits=min_qubits, max_qubits=max_qubits, skip_qubits=skip_qubits,\n",
+    "                max_circuits=max_circuits, num_shots=num_shots,\n",
+    "                backend_id=backend_id, provider_backend=provider_backend,\n",
+    "                hub=hub, group=group, project=project, exec_options=exec_options,\n",
+    "                api=api)"
    ]
   },
   {
diff --git a/bernstein-vazirani/bv_benchmark.py b/bernstein-vazirani/bv_benchmark.py
index 355e5508..5d3fce1a 100644
--- a/bernstein-vazirani/bv_benchmark.py
+++ b/bernstein-vazirani/bv_benchmark.py
@@ -6,7 +6,7 @@
 # This benchmark program runs at the top level of the named benchmark directory.
 # It uses the "api" parameter to select the API to be used for kernel construction and execution.
 
-import sys
+import os, sys
 import time
 import numpy as np
 
@@ -15,22 +15,28 @@
 # Configure the QED-C Benchmark package for use with the given API
 def qedc_benchmarks_init(api: str = "qiskit"):
 
-    if api == None: api = "qiskit"
+	if api == None: api = "qiskit"
 
-    sys.path[1:1] = [ f"{api}" ]
-    sys.path[1:1] = [ "_common", f"_common/{api}" ]
-    sys.path[1:1] = [ "../_common", f"../_common/{api}" ]
+	current_dir = os.path.dirname(os.path.abspath(__file__))
+	down_dir = os.path.abspath(os.path.join(current_dir, f"{api}"))
+	sys.path = [down_dir] + [p for p in sys.path if p != down_dir]
 
-    import execute as ex
-    globals()["ex"] = ex
+	up_dir = os.path.abspath(os.path.join(current_dir, ".."))
+	common_dir = os.path.abspath(os.path.join(up_dir, "_common"))
+	sys.path = [common_dir] + [p for p in sys.path if p != common_dir]
+	
+	api_dir = os.path.abspath(os.path.join(common_dir, f"{api}"))
+	sys.path = [api_dir] + [p for p in sys.path if p != api_dir]
 
-    import metrics as metrics
-    globals()["metrics"] = metrics
+	import execute as ex
+	globals()["ex"] = ex
 
-    from bv_kernel import BersteinVazirani, kernel_draw
-    
-    return BersteinVazirani, kernel_draw
+	import metrics as metrics
+	globals()["metrics"] = metrics
 
+	from bv_kernel import BersteinVazirani, kernel_draw
+	
+	return BersteinVazirani, kernel_draw
 
 # Benchmark Name
 benchmark_name = "Bernstein-Vazirani"
@@ -41,208 +47,208 @@ def qedc_benchmarks_init(api: str = "qiskit"):
 
 # Variable for number of resets to perform after mid circuit measurements
 num_resets = 1
-    
+	
 # Routine to convert the secret integer into an array of integers, each representing one bit
 # DEVNOTE: do we need to convert to string, or can we just keep shifting?
 def str_to_ivec(input_size: int, s_int: int):
 
-    # convert the secret integer into a string so we can scan the characters
-    s = ('{0:0' + str(input_size) + 'b}').format(s_int)
-    
-    # create an array to hold one integer per bit
-    bitset = []
-    
-    # assign bits in reverse order of characters in string
-    for i in range(input_size):
-
-        if s[input_size - 1 - i] == '1':
-            bitset.append(1)
-        else:
-            bitset.append(0)
-    
-    return bitset
-    
-    
+	# convert the secret integer into a string so we can scan the characters
+	s = ('{0:0' + str(input_size) + 'b}').format(s_int)
+	
+	# create an array to hold one integer per bit
+	bitset = []
+	
+	# assign bits in reverse order of characters in string
+	for i in range(input_size):
+
+		if s[input_size - 1 - i] == '1':
+			bitset.append(1)
+		else:
+			bitset.append(0)
+	
+	return bitset
+	
+	
 ############### Result Data Analysis
 
 # Analyze and print measured results
 # Expected result is always the secret_int, so fidelity calc is simple
 def analyze_and_print_result (qc, result, num_qubits, secret_int, num_shots):
-    
-    # size of input is one less than available qubits
-    input_size = num_qubits - 1
-    
-    # obtain counts from the result object
-    counts = result.get_counts(qc)
-    if verbose: print(f"For secret int {secret_int} measured: {counts}")   
-    
-    # create the key that is expected to have all the measurements (for this circuit)
-    key = format(secret_int, f"0{input_size}b")
-    if verbose: print(f"... key = {key}")
-    
-    # correct distribution is measuring the key 100% of the time
-    correct_dist = {key: 1.0}
-
-    # use our polarization fidelity rescaling
-    fidelity = metrics.polarization_fidelity(counts, correct_dist)
-        
-    return counts, fidelity
+	
+	# size of input is one less than available qubits
+	input_size = num_qubits - 1
+	
+	# obtain counts from the result object
+	counts = result.get_counts(qc)
+	if verbose: print(f"For secret int {secret_int} measured: {counts}")   
+	
+	# create the key that is expected to have all the measurements (for this circuit)
+	key = format(secret_int, f"0{input_size}b")
+	if verbose: print(f"... key = {key}")
+	
+	# correct distribution is measuring the key 100% of the time
+	correct_dist = {key: 1.0}
+
+	# use our polarization fidelity rescaling
+	fidelity = metrics.polarization_fidelity(counts, correct_dist)
+		
+	return counts, fidelity
 
 ################ Benchmark Loop
 
 # Execute program with default parameters
 def run (min_qubits=3, max_qubits=6, skip_qubits=1, max_circuits=3, num_shots=100,
-        method=1, input_value=None,
-        backend_id=None, provider_backend=None,
-        hub="ibm-q", group="open", project="main", exec_options=None,
-        context=None, api=None):
-
-    # configure the QED-C Benchmark package for use with the given API
-    BersteinVazirani, kernel_draw = qedc_benchmarks_init(api)
-    
-    print(f"{benchmark_name} ({method}) Benchmark Program - Qiskit")
-
-    # validate parameters (smallest circuit is 3 qubits)
-    max_qubits = max(3, max_qubits)
-    min_qubits = min(max(3, min_qubits), max_qubits)
-    skip_qubits = max(1, skip_qubits)
-    #print(f"min, max qubits = {min_qubits} {max_qubits}")
-
-    # create context identifier
-    if context is None: context = f"{benchmark_name} ({method}) Benchmark"
-    
-    ##########
-    
-    # Variable for new qubit group ordering if using mid_circuit measurements
-    mid_circuit_qubit_group = []
-
-    # If using mid_circuit measurements, set transform qubit group to true
-    transform_qubit_group = True if method == 2 else False
-    
-    # Initialize metrics module
-    metrics.init_metrics()
-
-    # Define custom result handler
-    def execution_handler (qc, result, num_qubits, s_int, num_shots):  
-     
-        # determine fidelity of result set
-        num_qubits = int(num_qubits)
-        counts, fidelity = analyze_and_print_result(qc, result, num_qubits, int(s_int), num_shots)
-        metrics.store_metric(num_qubits, s_int, 'fidelity', fidelity)
-
-    # Initialize execution module using the execution result handler above and specified backend_id
-    ex.init_execution(execution_handler)
-    ex.set_execution_target(backend_id, provider_backend=provider_backend,
-            hub=hub, group=group, project=project, exec_options=exec_options,
-            context=context)
-
-    # for noiseless simulation, set noise model to be None
-    # ex.set_noise_model(None)
-
-    ##########
-    
-    # Execute Benchmark Program N times for multiple circuit sizes
-    # Accumulate metrics asynchronously as circuits complete
-    for num_qubits in range(min_qubits, max_qubits + 1, skip_qubits):
-    
-        input_size = num_qubits - 1
-        
-        # determine number of circuits to execute for this group
-        num_circuits = min(2**(input_size), max_circuits)
-        
-        print(f"************\nExecuting [{num_circuits}] circuits with num_qubits = {num_qubits}")
-        
-        # determine range of secret strings to loop over
-        if 2**(input_size) <= max_circuits:
-            s_range = list(range(num_circuits))
-        else:
-            # create selection larger than needed and remove duplicates
-            s_range = np.random.randint(1, 2**(input_size), num_circuits + 2)
-            s_range = list(set(s_range))[0:max_circuits]
-            
-        # loop over limited # of secret strings for this
-        for s_int in s_range:
-            s_int = int(s_int)
-        
-            # if user specifies input_value, use it instead
-            # DEVNOTE: if max_circuits used, this will generate separate bar for each num_circuits
-            if input_value is not None:
-                s_int = input_value
-                
-            # convert the secret int string to array of integers, each representing one bit
-            bitset = str_to_ivec(input_size, s_int)
-            if verbose: print(f"... s_int={s_int} bitset={bitset}")
-                
-            # If mid circuit, then add 2 to new qubit group since the circuit only uses 2 qubits
-            if method == 2:
-                mid_circuit_qubit_group.append(2)
-            
-            # create the circuit for given qubit size and secret string, store time metric
-            ts = time.time()
-            qc = BersteinVazirani(num_qubits, s_int, bitset, method)       
-            metrics.store_metric(num_qubits, s_int, 'create_time', time.time()-ts)
-            
-            # submit circuit for execution on target (simulator, cloud simulator, or hardware)
-            ex.submit_circuit(qc, num_qubits, s_int, shots=num_shots)
-              
-        # Wait for some active circuits to complete; report metrics when groups complete
-        ex.throttle_execution(metrics.finalize_group)  
-        
-    # Wait for all active circuits to complete; report metrics when groups complete
-    ex.finalize_execution(metrics.finalize_group)
-       
-    ##########
-    
-    # draw a sample circuit
-    kernel_draw()
-
-    # Plot metrics for all circuit sizes
-    metrics.plot_metrics(f"Benchmark Results - {benchmark_name} ({method}) - Qiskit",
-                         transform_qubit_group = transform_qubit_group, new_qubit_group = mid_circuit_qubit_group)
+		method=1, input_value=None,
+		backend_id=None, provider_backend=None,
+		hub="ibm-q", group="open", project="main", exec_options=None,
+		context=None, api=None):
+
+	# configure the QED-C Benchmark package for use with the given API
+	BersteinVazirani, kernel_draw = qedc_benchmarks_init(api)
+	
+	print(f"{benchmark_name} ({method}) Benchmark Program - Qiskit")
+
+	# validate parameters (smallest circuit is 3 qubits)
+	max_qubits = max(3, max_qubits)
+	min_qubits = min(max(3, min_qubits), max_qubits)
+	skip_qubits = max(1, skip_qubits)
+	#print(f"min, max qubits = {min_qubits} {max_qubits}")
+
+	# create context identifier
+	if context is None: context = f"{benchmark_name} ({method}) Benchmark"
+	
+	##########
+	
+	# Variable for new qubit group ordering if using mid_circuit measurements
+	mid_circuit_qubit_group = []
+
+	# If using mid_circuit measurements, set transform qubit group to true
+	transform_qubit_group = True if method == 2 else False
+	
+	# Initialize metrics module
+	metrics.init_metrics()
+
+	# Define custom result handler
+	def execution_handler (qc, result, num_qubits, s_int, num_shots):  
+	 
+		# determine fidelity of result set
+		num_qubits = int(num_qubits)
+		counts, fidelity = analyze_and_print_result(qc, result, num_qubits, int(s_int), num_shots)
+		metrics.store_metric(num_qubits, s_int, 'fidelity', fidelity)
+
+	# Initialize execution module using the execution result handler above and specified backend_id
+	ex.init_execution(execution_handler)
+	ex.set_execution_target(backend_id, provider_backend=provider_backend,
+			hub=hub, group=group, project=project, exec_options=exec_options,
+			context=context)
+
+	# for noiseless simulation, set noise model to be None
+	# ex.set_noise_model(None)
+
+	##########
+	
+	# Execute Benchmark Program N times for multiple circuit sizes
+	# Accumulate metrics asynchronously as circuits complete
+	for num_qubits in range(min_qubits, max_qubits + 1, skip_qubits):
+	
+		input_size = num_qubits - 1
+		
+		# determine number of circuits to execute for this group
+		num_circuits = min(2**(input_size), max_circuits)
+		
+		print(f"************\nExecuting [{num_circuits}] circuits with num_qubits = {num_qubits}")
+		
+		# determine range of secret strings to loop over
+		if 2**(input_size) <= max_circuits:
+			s_range = list(range(num_circuits))
+		else:
+			# create selection larger than needed and remove duplicates
+			s_range = np.random.randint(1, 2**(input_size), num_circuits + 2)
+			s_range = list(set(s_range))[0:max_circuits]
+			
+		# loop over limited # of secret strings for this
+		for s_int in s_range:
+			s_int = int(s_int)
+		
+			# if user specifies input_value, use it instead
+			# DEVNOTE: if max_circuits used, this will generate separate bar for each num_circuits
+			if input_value is not None:
+				s_int = input_value
+				
+			# convert the secret int string to array of integers, each representing one bit
+			bitset = str_to_ivec(input_size, s_int)
+			if verbose: print(f"... s_int={s_int} bitset={bitset}")
+				
+			# If mid circuit, then add 2 to new qubit group since the circuit only uses 2 qubits
+			if method == 2:
+				mid_circuit_qubit_group.append(2)
+			
+			# create the circuit for given qubit size and secret string, store time metric
+			ts = time.time()
+			qc = BersteinVazirani(num_qubits, s_int, bitset, method)	   
+			metrics.store_metric(num_qubits, s_int, 'create_time', time.time()-ts)
+			
+			# submit circuit for execution on target (simulator, cloud simulator, or hardware)
+			ex.submit_circuit(qc, num_qubits, s_int, shots=num_shots)
+			  
+		# Wait for some active circuits to complete; report metrics when groups complete
+		ex.throttle_execution(metrics.finalize_group)  
+		
+	# Wait for all active circuits to complete; report metrics when groups complete
+	ex.finalize_execution(metrics.finalize_group)
+	   
+	##########
+	
+	# draw a sample circuit
+	kernel_draw()
+
+	# Plot metrics for all circuit sizes
+	metrics.plot_metrics(f"Benchmark Results - {benchmark_name} ({method}) - Qiskit",
+						 transform_qubit_group = transform_qubit_group, new_qubit_group = mid_circuit_qubit_group)
 
 #######################
 # MAIN
 
 import argparse
 def get_args():
-    parser = argparse.ArgumentParser(description="Bernstei-Vazirani Benchmark")
-    parser.add_argument("--api", "-a", default=None, help="Programming API", type=str)
-    parser.add_argument("--target", "-t", default=None, help="Target Backend", type=str)
-    parser.add_argument("--backend_id", "-b", default=None, help="Backend Identifier", type=str)
-    parser.add_argument("--num_shots", "-s", default=100, help="Number of shots", type=int)
-    parser.add_argument("--num_qubits", "-n", default=0, help="Number of qubits", type=int)
-    parser.add_argument("--min_qubits", "-min", default=3, help="Minimum number of qubits", type=int)
-    parser.add_argument("--max_qubits", "-max", default=8, help="Maximum number of qubits", type=int)
-    parser.add_argument("--skip_qubits", "-k", default=1, help="Number of qubits to skip", type=int)
-    parser.add_argument("--max_circuits", "-c", default=3, help="Maximum circuit repetitions", type=int)  
-    parser.add_argument("--method", "-m", default=1, help="Algorithm Method", type=int)
-    parser.add_argument("--input_value", "-i", default=None, help="Fixed Input Value", type=int)
-    parser.add_argument("--nonoise", "-non", action="store_true", help="Use Noiseless Simulator")
-    parser.add_argument("--verbose", "-v", action="store_true", help="Verbose")
-    return parser.parse_args()
-    
+	parser = argparse.ArgumentParser(description="Bernstei-Vazirani Benchmark")
+	parser.add_argument("--api", "-a", default=None, help="Programming API", type=str)
+	parser.add_argument("--target", "-t", default=None, help="Target Backend", type=str)
+	parser.add_argument("--backend_id", "-b", default=None, help="Backend Identifier", type=str)
+	parser.add_argument("--num_shots", "-s", default=100, help="Number of shots", type=int)
+	parser.add_argument("--num_qubits", "-n", default=0, help="Number of qubits", type=int)
+	parser.add_argument("--min_qubits", "-min", default=3, help="Minimum number of qubits", type=int)
+	parser.add_argument("--max_qubits", "-max", default=8, help="Maximum number of qubits", type=int)
+	parser.add_argument("--skip_qubits", "-k", default=1, help="Number of qubits to skip", type=int)
+	parser.add_argument("--max_circuits", "-c", default=3, help="Maximum circuit repetitions", type=int)  
+	parser.add_argument("--method", "-m", default=1, help="Algorithm Method", type=int)
+	parser.add_argument("--input_value", "-i", default=None, help="Fixed Input Value", type=int)
+	parser.add_argument("--nonoise", "-non", action="store_true", help="Use Noiseless Simulator")
+	parser.add_argument("--verbose", "-v", action="store_true", help="Verbose")
+	return parser.parse_args()
+	
 # if main, execute method
 if __name__ == '__main__': 
-    args = get_args()
-    
-    # configure the QED-C Benchmark package for use with the given API
-    # (done here so we can set verbose for now)
-    BersteinVazirani, kernel_draw = qedc_benchmarks_init(args.api)
-    
-    # special argument handling
-    ex.verbose = args.verbose
-    verbose = args.verbose
-    
-    if args.num_qubits > 0: args.min_qubits = args.max_qubits = args.num_qubits
-    
-    # execute benchmark program
-    run(min_qubits=args.min_qubits, max_qubits=args.max_qubits,
-        skip_qubits=args.skip_qubits, max_circuits=args.max_circuits,
-        num_shots=args.num_shots,
-        method=args.method,
-        input_value=args.input_value,
-        backend_id=args.backend_id,
-        exec_options = {"noise_model" : None} if args.nonoise else {},
-        api=args.api
-        )
+	args = get_args()
+	
+	# configure the QED-C Benchmark package for use with the given API
+	# (done here so we can set verbose for now)
+	BersteinVazirani, kernel_draw = qedc_benchmarks_init(args.api)
+	
+	# special argument handling
+	ex.verbose = args.verbose
+	verbose = args.verbose
+	
+	if args.num_qubits > 0: args.min_qubits = args.max_qubits = args.num_qubits
+	
+	# execute benchmark program
+	run(min_qubits=args.min_qubits, max_qubits=args.max_qubits,
+		skip_qubits=args.skip_qubits, max_circuits=args.max_circuits,
+		num_shots=args.num_shots,
+		method=args.method,
+		input_value=args.input_value,
+		backend_id=args.backend_id,
+		exec_options = {"noise_model" : None} if args.nonoise else {},
+		api=args.api
+		)
    
diff --git a/bernstein-vazirani/cudaq/bv_kernel.py b/bernstein-vazirani/cudaq/bv_kernel.py
index 3818f724..745299b9 100644
--- a/bernstein-vazirani/cudaq/bv_kernel.py
+++ b/bernstein-vazirani/cudaq/bv_kernel.py
@@ -107,7 +107,7 @@ def BersteinVazirani (num_qubits: int, secret_int: int, hidden_bits: List[int],
     qc = [bv_kernel, [num_qubits, secret_int, hidden_bits, method]]
     
     global QC_
-    if num_qubits <= 9:
+    if num_qubits <= 6:
         QC_ = qc
 
     return qc
diff --git a/deutsch-jozsa/qiskit/dj_benchmark.py b/deutsch-jozsa/qiskit/dj_benchmark.py
index 93e6e74d..fd607bf6 100644
--- a/deutsch-jozsa/qiskit/dj_benchmark.py
+++ b/deutsch-jozsa/qiskit/dj_benchmark.py
@@ -149,7 +149,7 @@ def analyze_and_print_result (qc, result, num_qubits, type, num_shots):
 
 # Execute program with default parameters
 def run (min_qubits=3, max_qubits=8, skip_qubits=1, max_circuits=3, num_shots=100,
-        backend_id='qasm_simulator', provider_backend=None,
+        backend_id=None, provider_backend=None,
         hub="ibm-q", group="open", project="main", exec_options=None,
         context=None):
 
diff --git a/hhl/qiskit/hhl_benchmark.py b/hhl/qiskit/hhl_benchmark.py
index ff56f3f5..333dfa7b 100644
--- a/hhl/qiskit/hhl_benchmark.py
+++ b/hhl/qiskit/hhl_benchmark.py
@@ -650,7 +650,7 @@ def analyze_and_print_result (qc, result, num_qubits, s_int, num_shots):
 
 def run (min_qubits=3, max_qubits=6, skip_qubits=1, max_circuits=3, num_shots=100,
         method = 1, use_best_widths=True, min_register_qubits=1,
-        backend_id='qasm_simulator', provider_backend=None,
+        backend_id=None, provider_backend=None,
         hub="ibm-q", group="open", project="main", exec_options=None,
         context=None):  
 
@@ -707,7 +707,7 @@ def run2 (min_input_qubits=1, max_input_qubits=3, skip_qubits=1,
         min_clock_qubits=1, max_clock_qubits=3,
         max_circuits=3, num_shots=100,
         method=2, use_best_widths=False, min_register_qubits=1,
-        backend_id='qasm_simulator', provider_backend=None,
+        backend_id=None, provider_backend=None,
         hub="ibm-q", group="open", project="main", exec_options=None,
         context=None):  
     
diff --git a/maxcut/qiskit/auxiliary_functions.py b/maxcut/qiskit/auxiliary_functions.py
index 9cf9fc94..91d6128f 100644
--- a/maxcut/qiskit/auxiliary_functions.py
+++ b/maxcut/qiskit/auxiliary_functions.py
@@ -184,7 +184,7 @@ def plot_worst_best_init_conditions(worst_dict, best_dict):
 
 
 def radar_plot(min_qubits=4, max_qubits=6, num_shots=1000, restarts=10, objective_func_type='approx_ratio',
-               rounds=1, degree=3, backend_id='qasm_simulator', provider_backend=None,
+               rounds=1, degree=3, backend_id=None, provider_backend=None,
                hub="ibm-q", group="open", project="main", exec_options=None,
                ):
 
diff --git a/maxcut/qiskit/maxcut_benchmark.py b/maxcut/qiskit/maxcut_benchmark.py
index 4c6a4502..312c69fe 100644
--- a/maxcut/qiskit/maxcut_benchmark.py
+++ b/maxcut/qiskit/maxcut_benchmark.py
@@ -913,7 +913,7 @@ def run (min_qubits=3, max_qubits=6, skip_qubits=2,
         fixed_metrics={}, num_x_bins=15, y_size=None, x_size=None, use_fixed_angles=False,
         objective_func_type = 'approx_ratio', plot_results = True,
         save_res_to_file = False, save_final_counts = False, detailed_save_names = False, comfort=False,
-        backend_id='qasm_simulator', provider_backend=None, eta=0.5,
+        backend_id=None, provider_backend=None, eta=0.5,
         hub="ibm-q", group="open", project="main", exec_options=None,
         context=None,
         _instances=None):
diff --git a/monte-carlo/qiskit/mc_benchmark.py b/monte-carlo/qiskit/mc_benchmark.py
index c69a91dd..7b5b8101 100644
--- a/monte-carlo/qiskit/mc_benchmark.py
+++ b/monte-carlo/qiskit/mc_benchmark.py
@@ -120,7 +120,7 @@ def square_on_objective(qc, qr):
         qc.cx(control, num_state_qubits)
 
 def state_prep(qc, qr, target_dist, num_state_qubits):
-    """
+    r"""
     Use controlled Ry gates to construct the superposition Sum \sqrt{p_i} |i>
     """
     r_probs = mc_utils.region_probs(target_dist, num_state_qubits)
@@ -362,7 +362,7 @@ def expectation_from_bits(bits, num_qubits, num_shots, method):
 # Execute program with default parameters
 def run(min_qubits=MIN_QUBITS, max_qubits=10, skip_qubits=1, max_circuits=1, num_shots=100,
         epsilon=0.05, degree=2, num_state_qubits=MIN_STATE_QUBITS, method = 2, # default, not exposed to users
-        backend_id='qasm_simulator', provider_backend=None,
+        backend_id=None, provider_backend=None,
         hub="ibm-q", group="open", project="main", exec_options=None,
         context=None):
 
@@ -505,7 +505,7 @@ def get_args():
     parser.add_argument("--skip_qubits", "-k", default=1, help="Number of qubits to skip", type=int)
     parser.add_argument("--max_circuits", "-c", default=3, help="Maximum circuit repetitions", type=int)  
     parser.add_argument("--method", "-m", default=1, help="Algorithm Method", type=int)
-    parser.add_argument("--num_state_qubits", "-nsq", default=0.0, help="Number of State Qubits", type=int)
+    parser.add_argument("--num_state_qubits", "-nsq", default=1, help="Number of State Qubits", type=int)
     parser.add_argument("--nonoise", "-non", action="store_true", help="Use Noiseless Simulator")
     parser.add_argument("--verbose", "-v", action="store_true", help="Verbose")
     return parser.parse_args()
diff --git a/phase-estimation/cudaq/pe_kernel.py b/phase-estimation/cudaq/pe_kernel.py
index 75483797..3f134f81 100644
--- a/phase-estimation/cudaq/pe_kernel.py
+++ b/phase-estimation/cudaq/pe_kernel.py
@@ -94,7 +94,7 @@ def PhaseEstimation (num_qubits: int, theta: float):
     qc = [pe_kernel, [num_qubits, theta]]
     
     global QC_
-    if num_qubits <= 9:
+    if num_qubits <= 6:
         QC_ = qc
 
     return qc
diff --git a/phase-estimation/pe_benchmark.py b/phase-estimation/pe_benchmark.py
index 887812cf..2de3b5b3 100644
--- a/phase-estimation/pe_benchmark.py
+++ b/phase-estimation/pe_benchmark.py
@@ -3,7 +3,7 @@
 (C) Quantum Economic Development Consortium (QED-C) 2024.
 '''
 
-import sys
+import os, sys
 import time
 import numpy as np
 
@@ -12,23 +12,30 @@
 # Configure the QED-C Benchmark package for use with the given API
 def qedc_benchmarks_init(api: str = "qiskit"):
 
-    if api == None: api = "qiskit"
+	if api == None: api = "qiskit"
 
-    sys.path[1:1] = [ f"{api}" ]
-    sys.path[1:1] = [ "_common", f"_common/{api}" ]
-    sys.path[1:1] = [ "../_common", f"../_common/{api}" ]
+	current_dir = os.path.dirname(os.path.abspath(__file__))
+	down_dir = os.path.abspath(os.path.join(current_dir, f"{api}"))
+	sys.path = [down_dir] + [p for p in sys.path if p != down_dir]
 
-    import execute as ex
-    globals()["ex"] = ex
+	up_dir = os.path.abspath(os.path.join(current_dir, ".."))
+	common_dir = os.path.abspath(os.path.join(up_dir, "_common"))
+	sys.path = [common_dir] + [p for p in sys.path if p != common_dir]
+	
+	api_dir = os.path.abspath(os.path.join(common_dir, f"{api}"))
+	sys.path = [api_dir] + [p for p in sys.path if p != api_dir]
 
-    import metrics as metrics
-    globals()["metrics"] = metrics
+	import execute as ex
+	globals()["ex"] = ex
 
-    from pe_kernel import PhaseEstimation, kernel_draw
-    
-    return PhaseEstimation, kernel_draw
-    
-    
+	import metrics as metrics
+	globals()["metrics"] = metrics
+
+	from pe_kernel import PhaseEstimation, kernel_draw
+	
+	return PhaseEstimation, kernel_draw
+	
+	
 # Benchmark Name
 benchmark_name = "Phase Estimation"
 
@@ -43,164 +50,164 @@ def qedc_benchmarks_init(api: str = "qiskit"):
 # Expected result is always theta, so fidelity calc is simple
 def analyze_and_print_result(qc, result, num_counting_qubits, theta, num_shots):
 
-    # get results as measured counts
-    counts = result.get_counts(qc)
-
-    # calculate expected output histogram
-    correct_dist = theta_to_bitstring(theta, num_counting_qubits)
-    
-    # generate thermal_dist and ap form of thermal_dist to be comparable to correct_dist
-    if num_counting_qubits < 15:
-        thermal_dist = metrics.uniform_dist(num_counting_qubits)
-        app_thermal_dist = bitstring_to_theta(thermal_dist, num_counting_qubits)
-    else :
-        thermal_dist = None
-        app_thermal_dist = None
-        
-    # convert counts expectation to app form for visibility
-    # app form of correct distribution is measuring theta correctly 100% of the time
-    app_counts = bitstring_to_theta(counts, num_counting_qubits)
-    app_correct_dist = {theta: 1.0} 
-    
-    if verbose:
-        print(f"For theta {theta}, expected: {correct_dist} measured: {counts}")
-        #print(f"   ... For theta {theta} thermal_dist: {thermal_dist}")
-        print(f"For theta {theta}, app expected: {app_correct_dist} measured: {app_counts}")
-        #print(f"   ... For theta {theta} app_thermal_dist: {app_thermal_dist}")
-        
-    # use polarization fidelity with rescaling
-    fidelity = metrics.polarization_fidelity(counts, correct_dist, thermal_dist)
-    
-    # use polarization fidelity with rescaling
-    fidelity = metrics.polarization_fidelity(counts, correct_dist, thermal_dist)
-    #fidelity = metrics.polarization_fidelity(app_counts, app_correct_dist, app_thermal_dist)
-    
-    hf_fidelity = metrics.hellinger_fidelity_with_expected(counts, correct_dist)
-    
-    if verbose: print(f"  ... fidelity: {fidelity}  hf_fidelity: {hf_fidelity}")
-        
-    return counts, fidelity
+	# get results as measured counts
+	counts = result.get_counts(qc)
+
+	# calculate expected output histogram
+	correct_dist = theta_to_bitstring(theta, num_counting_qubits)
+	
+	# generate thermal_dist and ap form of thermal_dist to be comparable to correct_dist
+	if num_counting_qubits < 15:
+		thermal_dist = metrics.uniform_dist(num_counting_qubits)
+		app_thermal_dist = bitstring_to_theta(thermal_dist, num_counting_qubits)
+	else :
+		thermal_dist = None
+		app_thermal_dist = None
+		
+	# convert counts expectation to app form for visibility
+	# app form of correct distribution is measuring theta correctly 100% of the time
+	app_counts = bitstring_to_theta(counts, num_counting_qubits)
+	app_correct_dist = {theta: 1.0} 
+	
+	if verbose:
+		print(f"For theta {theta}, expected: {correct_dist} measured: {counts}")
+		#print(f"	... For theta {theta} thermal_dist: {thermal_dist}")
+		print(f"For theta {theta}, app expected: {app_correct_dist} measured: {app_counts}")
+		#print(f"	... For theta {theta} app_thermal_dist: {app_thermal_dist}")
+		
+	# use polarization fidelity with rescaling
+	fidelity = metrics.polarization_fidelity(counts, correct_dist, thermal_dist)
+	
+	# use polarization fidelity with rescaling
+	fidelity = metrics.polarization_fidelity(counts, correct_dist, thermal_dist)
+	#fidelity = metrics.polarization_fidelity(app_counts, app_correct_dist, app_thermal_dist)
+	
+	hf_fidelity = metrics.hellinger_fidelity_with_expected(counts, correct_dist)
+	
+	if verbose: print(f"  ... fidelity: {fidelity}	hf_fidelity: {hf_fidelity}")
+		
+	return counts, fidelity
 
 # Convert theta to a bitstring distribution
 def theta_to_bitstring(theta, num_counting_qubits):
-    counts = {format( int(theta * (2**num_counting_qubits)), "0"+str(num_counting_qubits)+"b"): 1.0}
-    return counts
+	counts = {format( int(theta * (2**num_counting_qubits)), "0"+str(num_counting_qubits)+"b"): 1.0}
+	return counts
 
 # Convert bitstring to theta representation, useful for debugging
 def bitstring_to_theta(counts, num_counting_qubits):
-    theta_counts = {}
-    for item in counts.items():
-        key, r = item
-        theta = int(key,2) / (2**num_counting_qubits)
-        if theta not in theta_counts.keys():
-            theta_counts[theta] = 0
-        theta_counts[theta] += r
-    return theta_counts
+	theta_counts = {}
+	for item in counts.items():
+		key, r = item
+		theta = int(key,2) / (2**num_counting_qubits)
+		if theta not in theta_counts.keys():
+			theta_counts[theta] = 0
+		theta_counts[theta] += r
+	return theta_counts
 
 
 ################ Benchmark Loop
 
 # Execute program with default parameters
 def run(min_qubits=3, max_qubits=8, skip_qubits=1, max_circuits=3, num_shots=100,
-        init_phase=None,
-        backend_id=None, provider_backend=None,
-        hub="ibm-q", group="open", project="main", exec_options=None,
-        context=None, api=None):
-
-    # configure the QED-C Benchmark package for use with the given API
-    PhaseEstimation, kernel_draw = qedc_benchmarks_init(api)
-    
-    print(f"{benchmark_name} Benchmark Program - Qiskit")
-
-    num_state_qubits = 1 # default, not exposed to users, cannot be changed in current implementation
-
-    # validate parameters (smallest circuit is 3 qubits)
-    num_state_qubits = max(1, num_state_qubits)
-    if max_qubits < num_state_qubits + 2:
-        print(f"ERROR: PE Benchmark needs at least {num_state_qubits + 2} qubits to run")
-        return
-    min_qubits = max(max(3, min_qubits), num_state_qubits + 2)
-    skip_qubits = max(1, skip_qubits)
-    #print(f"min, max, state = {min_qubits} {max_qubits} {num_state_qubits}")
-
-    # create context identifier
-    if context is None: context = f"{benchmark_name} Benchmark"
-    
-    ##########
-    
-    # Initialize metrics module
-    metrics.init_metrics()
-
-    # Define custom result handler
-    def execution_handler(qc, result, num_qubits, theta, num_shots):
-
-        # determine fidelity of result set
-        num_counting_qubits = int(num_qubits) - 1
-        counts, fidelity = analyze_and_print_result(qc, result, num_counting_qubits, float(theta), num_shots)
-        metrics.store_metric(num_qubits, theta, 'fidelity', fidelity)
-
-    # Initialize execution module using the execution result handler above and specified backend_id
-    ex.init_execution(execution_handler)
-    ex.set_execution_target(backend_id, provider_backend=provider_backend,
-            hub=hub, group=group, project=project, exec_options=exec_options,
-            context=context)
-
-    ##########
-    
-    # Execute Benchmark Program N times for multiple circuit sizes
-    # Accumulate metrics asynchronously as circuits complete
-    for num_qubits in range(min_qubits, max_qubits + 1, skip_qubits):
-        
-        # reset random seed
-        np.random.seed(0)
-
-        # as circuit width grows, the number of counting qubits is increased
-        num_counting_qubits = num_qubits - num_state_qubits
-
-        # determine number of circuits to execute for this group
-        num_circuits = min(2 ** (num_counting_qubits), max_circuits)
-        
-        print(f"************\nExecuting [{num_circuits}] circuits with num_qubits = {num_qubits}")
-        
-        # determine range of secret strings to loop over
-        if 2**(num_counting_qubits) <= max_circuits:
-            theta_choices = list(range(num_circuits))
-        else:
-            theta_choices = np.random.randint(1, 2**(num_counting_qubits), num_circuits + 10)
-            theta_choices = list(set(theta_choices))[0:num_circuits]
-            
-        # scale choices to 1.0
-        theta_range = [i/(2**(num_counting_qubits)) for i in theta_choices]
-
-        # loop over limited # of random theta choices
-        for theta in theta_range:
-            theta = float(theta) 
-            
-            # if initial phase passed in, use it instead of random values
-            if init_phase:
-                theta = init_phase
-        
-            # create the circuit for given qubit size and theta, store time metric
-            ts = time.time()
-            qc = PhaseEstimation(num_qubits, theta)
-            metrics.store_metric(num_qubits, theta, 'create_time', time.time() - ts)
-            
-            # submit circuit for execution on target (simulator, cloud simulator, or hardware)
-            ex.submit_circuit(qc, num_qubits, theta, num_shots)
-
-        # Wait for some active circuits to complete; report metrics when groups complete
-        ex.throttle_execution(metrics.finalize_group)
-
-    # Wait for all active circuits to complete; report metrics when groups complete
-    ex.finalize_execution(metrics.finalize_group)
-
-    ##########
-
-    # draw a sample circuit
-    kernel_draw()
-
-    # Plot metrics for all circuit sizes
-    metrics.plot_metrics(f"Benchmark Results - {benchmark_name} - Qiskit")
+		init_phase=None,
+		backend_id=None, provider_backend=None,
+		hub="ibm-q", group="open", project="main", exec_options=None,
+		context=None, api=None):
+
+	# configure the QED-C Benchmark package for use with the given API
+	PhaseEstimation, kernel_draw = qedc_benchmarks_init(api)
+	
+	print(f"{benchmark_name} Benchmark Program - Qiskit")
+
+	num_state_qubits = 1 # default, not exposed to users, cannot be changed in current implementation
+
+	# validate parameters (smallest circuit is 3 qubits)
+	num_state_qubits = max(1, num_state_qubits)
+	if max_qubits < num_state_qubits + 2:
+		print(f"ERROR: PE Benchmark needs at least {num_state_qubits + 2} qubits to run")
+		return
+	min_qubits = max(max(3, min_qubits), num_state_qubits + 2)
+	skip_qubits = max(1, skip_qubits)
+	#print(f"min, max, state = {min_qubits} {max_qubits} {num_state_qubits}")
+
+	# create context identifier
+	if context is None: context = f"{benchmark_name} Benchmark"
+	
+	##########
+	
+	# Initialize metrics module
+	metrics.init_metrics()
+
+	# Define custom result handler
+	def execution_handler(qc, result, num_qubits, theta, num_shots):
+
+		# determine fidelity of result set
+		num_counting_qubits = int(num_qubits) - 1
+		counts, fidelity = analyze_and_print_result(qc, result, num_counting_qubits, float(theta), num_shots)
+		metrics.store_metric(num_qubits, theta, 'fidelity', fidelity)
+
+	# Initialize execution module using the execution result handler above and specified backend_id
+	ex.init_execution(execution_handler)
+	ex.set_execution_target(backend_id, provider_backend=provider_backend,
+			hub=hub, group=group, project=project, exec_options=exec_options,
+			context=context)
+
+	##########
+	
+	# Execute Benchmark Program N times for multiple circuit sizes
+	# Accumulate metrics asynchronously as circuits complete
+	for num_qubits in range(min_qubits, max_qubits + 1, skip_qubits):
+		
+		# reset random seed
+		np.random.seed(0)
+
+		# as circuit width grows, the number of counting qubits is increased
+		num_counting_qubits = num_qubits - num_state_qubits
+
+		# determine number of circuits to execute for this group
+		num_circuits = min(2 ** (num_counting_qubits), max_circuits)
+		
+		print(f"************\nExecuting [{num_circuits}] circuits with num_qubits = {num_qubits}")
+		
+		# determine range of secret strings to loop over
+		if 2**(num_counting_qubits) <= max_circuits:
+			theta_choices = list(range(num_circuits))
+		else:
+			theta_choices = np.random.randint(1, 2**(num_counting_qubits), num_circuits + 10)
+			theta_choices = list(set(theta_choices))[0:num_circuits]
+			
+		# scale choices to 1.0
+		theta_range = [i/(2**(num_counting_qubits)) for i in theta_choices]
+
+		# loop over limited # of random theta choices
+		for theta in theta_range:
+			theta = float(theta) 
+			
+			# if initial phase passed in, use it instead of random values
+			if init_phase:
+				theta = init_phase
+		
+			# create the circuit for given qubit size and theta, store time metric
+			ts = time.time()
+			qc = PhaseEstimation(num_qubits, theta)
+			metrics.store_metric(num_qubits, theta, 'create_time', time.time() - ts)
+			
+			# submit circuit for execution on target (simulator, cloud simulator, or hardware)
+			ex.submit_circuit(qc, num_qubits, theta, num_shots)
+
+		# Wait for some active circuits to complete; report metrics when groups complete
+		ex.throttle_execution(metrics.finalize_group)
+
+	# Wait for all active circuits to complete; report metrics when groups complete
+	ex.finalize_execution(metrics.finalize_group)
+
+	##########
+
+	# draw a sample circuit
+	kernel_draw()
+
+	# Plot metrics for all circuit sizes
+	metrics.plot_metrics(f"Benchmark Results - {benchmark_name} - Qiskit")
 
 
 #######################
@@ -208,45 +215,45 @@ def execution_handler(qc, result, num_qubits, theta, num_shots):
 
 import argparse
 def get_args():
-    parser = argparse.ArgumentParser(description="Bernstei-Vazirani Benchmark")
-    parser.add_argument("--api", "-a", default=None, help="Programming API", type=str)
-    parser.add_argument("--target", "-t", default=None, help="Target Backend", type=str)
-    parser.add_argument("--backend_id", "-b", default=None, help="Backend Identifier", type=str)
-    parser.add_argument("--num_shots", "-s", default=100, help="Number of shots", type=int)
-    parser.add_argument("--num_qubits", "-n", default=0, help="Number of qubits", type=int)
-    parser.add_argument("--min_qubits", "-min", default=3, help="Minimum number of qubits", type=int)
-    parser.add_argument("--max_qubits", "-max", default=8, help="Maximum number of qubits", type=int)
-    parser.add_argument("--skip_qubits", "-k", default=1, help="Number of qubits to skip", type=int)
-    parser.add_argument("--max_circuits", "-c", default=3, help="Maximum circuit repetitions", type=int)  
-    parser.add_argument("--method", "-m", default=1, help="Algorithm Method", type=int)
-    parser.add_argument("--init_phase", "-p", default=0.0, help="Input Phase Value", type=float)
-    parser.add_argument("--nonoise", "-non", action="store_true", help="Use Noiseless Simulator")
-    parser.add_argument("--verbose", "-v", action="store_true", help="Verbose")
-    return parser.parse_args()
-    
+	parser = argparse.ArgumentParser(description="Bernstei-Vazirani Benchmark")
+	parser.add_argument("--api", "-a", default=None, help="Programming API", type=str)
+	parser.add_argument("--target", "-t", default=None, help="Target Backend", type=str)
+	parser.add_argument("--backend_id", "-b", default=None, help="Backend Identifier", type=str)
+	parser.add_argument("--num_shots", "-s", default=100, help="Number of shots", type=int)
+	parser.add_argument("--num_qubits", "-n", default=0, help="Number of qubits", type=int)
+	parser.add_argument("--min_qubits", "-min", default=3, help="Minimum number of qubits", type=int)
+	parser.add_argument("--max_qubits", "-max", default=8, help="Maximum number of qubits", type=int)
+	parser.add_argument("--skip_qubits", "-k", default=1, help="Number of qubits to skip", type=int)
+	parser.add_argument("--max_circuits", "-c", default=3, help="Maximum circuit repetitions", type=int)  
+	parser.add_argument("--method", "-m", default=1, help="Algorithm Method", type=int)
+	parser.add_argument("--init_phase", "-p", default=0.0, help="Input Phase Value", type=float)
+	parser.add_argument("--nonoise", "-non", action="store_true", help="Use Noiseless Simulator")
+	parser.add_argument("--verbose", "-v", action="store_true", help="Verbose")
+	return parser.parse_args()
+	
 # if main, execute method
 if __name__ == '__main__': 
-    args = get_args()
-    
-    # configure the QED-C Benchmark package for use with the given API
-    # (done here so we can set verbose for now)
-    PhaseEstimation, kernel_draw = qedc_benchmarks_init(args.api)
-    
-    # special argument handling
-    ex.verbose = args.verbose
-    verbose = args.verbose
-    
-    if args.num_qubits > 0: args.min_qubits = args.max_qubits = args.num_qubits
-    
-    # execute benchmark program
-    run(min_qubits=args.min_qubits, max_qubits=args.max_qubits,
-        skip_qubits=args.skip_qubits, max_circuits=args.max_circuits,
-        num_shots=args.num_shots,
-        #method=args.method,
-        init_phase=args.init_phase,
-        backend_id=args.backend_id,
-        exec_options = {"noise_model" : None} if args.nonoise else {},
-        api=args.api
-        )
+	args = get_args()
+	
+	# configure the QED-C Benchmark package for use with the given API
+	# (done here so we can set verbose for now)
+	PhaseEstimation, kernel_draw = qedc_benchmarks_init(args.api)
+	
+	# special argument handling
+	ex.verbose = args.verbose
+	verbose = args.verbose
+	
+	if args.num_qubits > 0: args.min_qubits = args.max_qubits = args.num_qubits
+	
+	# execute benchmark program
+	run(min_qubits=args.min_qubits, max_qubits=args.max_qubits,
+		skip_qubits=args.skip_qubits, max_circuits=args.max_circuits,
+		num_shots=args.num_shots,
+		#method=args.method,
+		init_phase=args.init_phase,
+		backend_id=args.backend_id,
+		exec_options = {"noise_model" : None} if args.nonoise else {},
+		api=args.api
+		)
    
 
diff --git a/quantum-fourier-transform/cudaq/qft_kernel.py b/quantum-fourier-transform/cudaq/qft_kernel.py
index a831e87a..40e7ab95 100644
--- a/quantum-fourier-transform/cudaq/qft_kernel.py
+++ b/quantum-fourier-transform/cudaq/qft_kernel.py
@@ -123,7 +123,7 @@ def QuantumFourierTransform (num_qubits: int, secret_int: int, init_phase: List[
 	qc = [qft_kernel, [num_qubits, secret_int, init_phase, method]]
 	
 	global QC_
-	if num_qubits <= 9:
+	if num_qubits <= 6:
 		QC_ = qc
 
 	return qc
diff --git a/quantum-fourier-transform/qft_benchmark.py b/quantum-fourier-transform/qft_benchmark.py
index 12836b3c..13de9be2 100644
--- a/quantum-fourier-transform/qft_benchmark.py
+++ b/quantum-fourier-transform/qft_benchmark.py
@@ -6,7 +6,7 @@
 # This benchmark program runs at the top level of the named benchmark directory.
 # It uses the "api" parameter to select the API to be used for kernel construction and execution.
 
-import sys
+import os, sys
 import time
 import numpy as np
 
@@ -15,25 +15,32 @@
 # Configure the QED-C Benchmark package for use with the given API
 def qedc_benchmarks_init(api: str = "qiskit"):
 
-    if api == None: api = "qiskit"
+	if api == None: api = "qiskit"
 
-    sys.path[1:1] = [ f"{api}" ]
-    sys.path[1:1] = [ "_common", f"_common/{api}" ]
-    sys.path[1:1] = [ "../_common", f"../_common/{api}" ]
+	current_dir = os.path.dirname(os.path.abspath(__file__))
+	down_dir = os.path.abspath(os.path.join(current_dir, f"{api}"))
+	sys.path = [down_dir] + [p for p in sys.path if p != down_dir]
 
-    import execute as ex
-    globals()["ex"] = ex
+	up_dir = os.path.abspath(os.path.join(current_dir, ".."))
+	common_dir = os.path.abspath(os.path.join(up_dir, "_common"))
+	sys.path = [common_dir] + [p for p in sys.path if p != common_dir]
+	
+	api_dir = os.path.abspath(os.path.join(common_dir, f"{api}"))
+	sys.path = [api_dir] + [p for p in sys.path if p != api_dir]
 
-    import metrics as metrics
-    globals()["metrics"] = metrics
+	import execute as ex
+	globals()["ex"] = ex
 
-    from qft_kernel import QuantumFourierTransform, kernel_draw
-    
-    return QuantumFourierTransform, kernel_draw
+	import metrics as metrics
+	globals()["metrics"] = metrics
+
+	from qft_kernel import QuantumFourierTransform, kernel_draw
+	
+	return QuantumFourierTransform, kernel_draw
 
 
 # Benchmark Name
-benchmark_name = "Quantum-Fourier-Transform"
+benchmark_name = "Quantum Fourier Transform"
 
 np.random.seed(0)
 
@@ -41,248 +48,248 @@ def qedc_benchmarks_init(api: str = "qiskit"):
 
 # Variable for number of resets to perform after mid circuit measurements
 num_resets = 1
-    
+	
 # Routine to convert the secret integer into an array of integers, each representing one bit
 # DEVNOTE: do we need to convert to string, or can we just keep shifting?
 def str_to_ivec(input_size: int, s_int: int):
 
-    # convert the secret integer into a string so we can scan the characters
-    s = ('{0:0' + str(input_size) + 'b}').format(s_int)
-    
-    # create an array to hold one integer per bit
-    bitset = []
-    
-    # assign bits in reverse order of characters in string
-    for i in range(input_size):
-
-        if s[input_size - 1 - i] == '1':
-            bitset.append(1)
-        else:
-            bitset.append(0)
-    
-    return bitset
-    
-    
+	# convert the secret integer into a string so we can scan the characters
+	s = ('{0:0' + str(input_size) + 'b}').format(s_int)
+	
+	# create an array to hold one integer per bit
+	bitset = []
+	
+	# assign bits in reverse order of characters in string
+	for i in range(input_size):
+
+		if s[input_size - 1 - i] == '1':
+			bitset.append(1)
+		else:
+			bitset.append(0)
+	
+	return bitset
+	
+	
 ############### Result Data Analysis
 
 # Define expected distribution calculated from applying the iqft to the prepared secret_int state
 def expected_dist(num_qubits, secret_int, counts):
-    dist = {}
-    s = num_qubits - secret_int
-    for key in counts.keys():
-        if key[(num_qubits-secret_int):] == ''.zfill(secret_int):
-            dist[key] = 1/(2**s)
-    return dist
+	dist = {}
+	s = num_qubits - secret_int
+	for key in counts.keys():
+		if key[(num_qubits-secret_int):] == ''.zfill(secret_int):
+			dist[key] = 1/(2**s)
+	return dist
 
 ############### Result Data Analysis
 
 # Analyze and print measured results
 def analyze_and_print_result (qc, result, num_qubits, secret_int, num_shots, method):
 
-    # obtain counts from the result object
-    counts = result.get_counts(qc)
-    if verbose: print(f"For secret int {secret_int} measured: {counts}") 
+	# obtain counts from the result object
+	counts = result.get_counts(qc)
+	if verbose: print(f"For secret int {secret_int} measured: {counts}") 
 
-    # For method 1, expected result is always the secret_int
-    if method==1:
-        
-        # add one to the secret_int to compensate for the extra rotations done between QFT and IQFT
-        secret_int_plus_one = (secret_int + 1) % (2 ** num_qubits)
+	# For method 1, expected result is always the secret_int
+	if method==1:
+		
+		# add one to the secret_int to compensate for the extra rotations done between QFT and IQFT
+		secret_int_plus_one = (secret_int + 1) % (2 ** num_qubits)
 
-        # create the key that is expected to have all the measurements (for this circuit)
-        key = format(secret_int_plus_one, f"0{num_qubits}b")
+		# create the key that is expected to have all the measurements (for this circuit)
+		key = format(secret_int_plus_one, f"0{num_qubits}b")
 
-        # correct distribution is measuring the key 100% of the time
-        correct_dist = {key: 1.0}
-        if verbose: print(f"... correct_dist: {correct_dist}")
-        
-    # For method 2, expected result is always the secret_int
-    elif method==2:
+		# correct distribution is measuring the key 100% of the time
+		correct_dist = {key: 1.0}
+		if verbose: print(f"... correct_dist: {correct_dist}")
+		
+	# For method 2, expected result is always the secret_int
+	elif method==2:
 
-        # create the key that is expected to have all the measurements (for this circuit)
-        key = format(secret_int, f"0{num_qubits}b")
+		# create the key that is expected to have all the measurements (for this circuit)
+		key = format(secret_int, f"0{num_qubits}b")
 
-        # correct distribution is measuring the key 100% of the time
-        correct_dist = {key: 1.0}
-    
-    # For method 3, correct_dist is a distribution with more than one value
-    elif method==3:
+		# correct distribution is measuring the key 100% of the time
+		correct_dist = {key: 1.0}
+	
+	# For method 3, correct_dist is a distribution with more than one value
+	elif method==3:
 
-        # correct_dist is from the expected dist
-        correct_dist = expected_dist(num_qubits, secret_int, counts)
-            
-    # use our polarization fidelity rescaling
-    fidelity = metrics.polarization_fidelity(counts, correct_dist)
+		# correct_dist is from the expected dist
+		correct_dist = expected_dist(num_qubits, secret_int, counts)
+			
+	# use our polarization fidelity rescaling
+	fidelity = metrics.polarization_fidelity(counts, correct_dist)
 
-    if verbose: print(f"... fidelity: {fidelity}")
+	if verbose: print(f"... fidelity: {fidelity}")
 
-    return counts, fidelity
+	return counts, fidelity
 
 
 ################ Benchmark Loop
 
 # Execute program with default parameters
 def run (min_qubits=2, max_qubits=8, skip_qubits=1, max_circuits=3, num_shots=100,
-        method=1, input_value=None,
-        backend_id=None, provider_backend=None,
-        hub="ibm-q", group="open", project="main", exec_options=None,
-        context=None, api=None):
-
-    # configure the QED-C Benchmark package for use with the given API
-    QuantumFourierTransform, kernel_draw = qedc_benchmarks_init(api)
-    
-    print(f"{benchmark_name} ({method}) Benchmark Program - Qiskit")
-
-    # validate parameters (smallest circuit is 2 qubits)
-    max_qubits = max(2, max_qubits)
-    min_qubits = min(max(2, min_qubits), max_qubits)
-    skip_qubits = max(1, skip_qubits)
-    #print(f"min, max qubits = {min_qubits} {max_qubits}")
-
-    # create context identifier
-    if context is None: context = f"{benchmark_name} ({method}) Benchmark"
-    
-    ##########
-    
-    # Initialize metrics module
-    metrics.init_metrics()
-
-    # Define custom result handler
-    def execution_handler (qc, result, input_size, s_int, num_shots):  
-     
-        # determine fidelity of result set
-        num_qubits = int(input_size)
-        counts, fidelity = analyze_and_print_result(qc, result, num_qubits, int(s_int), num_shots, method)
-        metrics.store_metric(input_size, s_int, 'fidelity', fidelity)
-
-    # Initialize execution module using the execution result handler above and specified backend_id
-    ex.init_execution(execution_handler)
-    ex.set_execution_target(backend_id, provider_backend=provider_backend,
-            hub=hub, group=group, project=project, exec_options=exec_options,
-            context=context)
-
-    ##########
-    
-    # Execute Benchmark Program N times for multiple circuit sizes
-    # Accumulate metrics asynchronously as circuits complete
-    for input_size in range(min_qubits, max_qubits + 1, skip_qubits):
-        
-        # reset random seed 
-        np.random.seed(0)
-
-        num_qubits = input_size
-
-        # determine number of circuits to execute for this group
-        # and determine range of secret strings to loop over
-        if method == 1 or method == 2:
-            num_circuits = min(2 ** (input_size), max_circuits)
-        
-            if 2**(input_size) <= max_circuits:
-                s_range = list(range(num_circuits))
-            else:
-                s_range = np.random.randint(0, 2**(input_size), num_circuits + 2)
-                s_range = list(set(s_range))[0:num_circuits]
-         
-        elif method == 3:
-            num_circuits = min(input_size, max_circuits)
-
-            if input_size <= max_circuits:
-                s_range = list(range(num_circuits))
-            else:
-                s_range = np.random.randint(0, 2**(input_size), num_circuits + 2)
-                s_range = list(set(s_range))[0:num_circuits]
-        
-        else:
-            sys.exit("Invalid QFT method")
-        
-        print(f"************\nExecuting [{num_circuits}] circuits with num_qubits = {num_qubits}")
-        
-        # determine range of secret strings to loop over
-        if 2**(input_size) <= max_circuits:
-            s_range = list(range(num_circuits))
-        else:
-            # create selection larger than needed and remove duplicates
-            s_range = np.random.randint(1, 2**(input_size), num_circuits + 2)
-            s_range = list(set(s_range))[0:max_circuits]
-            
-        # loop over limited # of secret strings for this
-        for s_int in s_range:
-            s_int = int(s_int)
-        
-            # if user specifies input_value, use it instead
-            # DEVNOTE: if max_circuits used, this will generate separate bar for each num_circuits
-            if input_value is not None:
-                s_int = input_value
-                
-            # convert the secret int string to array of integers, each representing one bit
-            bitset = str_to_ivec(input_size, s_int)
-            if verbose: print(f"... s_int={s_int} bitset={bitset}")
-            
-            # create the circuit for given qubit size and secret string, store time metric
-            ts = time.time()
-            qc = QuantumFourierTransform(num_qubits, s_int, bitset, method)       
-            metrics.store_metric(input_size, s_int, 'create_time', time.time()-ts)
-            
-            # submit circuit for execution on target (simulator, cloud simulator, or hardware)
-            ex.submit_circuit(qc, input_size, s_int, shots=num_shots)
-              
-        # Wait for some active circuits to complete; report metrics when groups complete
-        ex.throttle_execution(metrics.finalize_group)  
-        
-    # Wait for all active circuits to complete; report metrics when groups complete
-    ex.finalize_execution(metrics.finalize_group)
-       
-    ##########
-    
-    # draw a sample circuit
-    kernel_draw()
-
-    # Plot metrics for all circuit sizes                         
-    metrics.plot_metrics(f"Benchmark Results - {benchmark_name} ({method}) - Qiskit")
+		method=1, input_value=None,
+		backend_id=None, provider_backend=None,
+		hub="ibm-q", group="open", project="main", exec_options=None,
+		context=None, api=None):
+
+	# configure the QED-C Benchmark package for use with the given API
+	QuantumFourierTransform, kernel_draw = qedc_benchmarks_init(api)
+	
+	print(f"{benchmark_name} ({method}) Benchmark Program - Qiskit")
+
+	# validate parameters (smallest circuit is 2 qubits)
+	max_qubits = max(2, max_qubits)
+	min_qubits = min(max(2, min_qubits), max_qubits)
+	skip_qubits = max(1, skip_qubits)
+	#print(f"min, max qubits = {min_qubits} {max_qubits}")
+
+	# create context identifier
+	if context is None: context = f"{benchmark_name} ({method}) Benchmark"
+	
+	##########
+	
+	# Initialize metrics module
+	metrics.init_metrics()
+
+	# Define custom result handler
+	def execution_handler (qc, result, input_size, s_int, num_shots):  
+	 
+		# determine fidelity of result set
+		num_qubits = int(input_size)
+		counts, fidelity = analyze_and_print_result(qc, result, num_qubits, int(s_int), num_shots, method)
+		metrics.store_metric(input_size, s_int, 'fidelity', fidelity)
+
+	# Initialize execution module using the execution result handler above and specified backend_id
+	ex.init_execution(execution_handler)
+	ex.set_execution_target(backend_id, provider_backend=provider_backend,
+			hub=hub, group=group, project=project, exec_options=exec_options,
+			context=context)
+
+	##########
+	
+	# Execute Benchmark Program N times for multiple circuit sizes
+	# Accumulate metrics asynchronously as circuits complete
+	for input_size in range(min_qubits, max_qubits + 1, skip_qubits):
+		
+		# reset random seed 
+		np.random.seed(0)
+
+		num_qubits = input_size
+
+		# determine number of circuits to execute for this group
+		# and determine range of secret strings to loop over
+		if method == 1 or method == 2:
+			num_circuits = min(2 ** (input_size), max_circuits)
+		
+			if 2**(input_size) <= max_circuits:
+				s_range = list(range(num_circuits))
+			else:
+				s_range = np.random.randint(0, 2**(input_size), num_circuits + 2)
+				s_range = list(set(s_range))[0:num_circuits]
+		 
+		elif method == 3:
+			num_circuits = min(input_size, max_circuits)
+
+			if input_size <= max_circuits:
+				s_range = list(range(num_circuits))
+			else:
+				s_range = np.random.randint(0, 2**(input_size), num_circuits + 2)
+				s_range = list(set(s_range))[0:num_circuits]
+		
+		else:
+			sys.exit("Invalid QFT method")
+		
+		print(f"************\nExecuting [{num_circuits}] circuits with num_qubits = {num_qubits}")
+		
+		# determine range of secret strings to loop over
+		if 2**(input_size) <= max_circuits:
+			s_range = list(range(num_circuits))
+		else:
+			# create selection larger than needed and remove duplicates
+			s_range = np.random.randint(1, 2**(input_size), num_circuits + 2)
+			s_range = list(set(s_range))[0:max_circuits]
+			
+		# loop over limited # of secret strings for this
+		for s_int in s_range:
+			s_int = int(s_int)
+		
+			# if user specifies input_value, use it instead
+			# DEVNOTE: if max_circuits used, this will generate separate bar for each num_circuits
+			if input_value is not None:
+				s_int = input_value
+				
+			# convert the secret int string to array of integers, each representing one bit
+			bitset = str_to_ivec(input_size, s_int)
+			if verbose: print(f"... s_int={s_int} bitset={bitset}")
+			
+			# create the circuit for given qubit size and secret string, store time metric
+			ts = time.time()
+			qc = QuantumFourierTransform(num_qubits, s_int, bitset, method)		  
+			metrics.store_metric(input_size, s_int, 'create_time', time.time()-ts)
+			
+			# submit circuit for execution on target (simulator, cloud simulator, or hardware)
+			ex.submit_circuit(qc, input_size, s_int, shots=num_shots)
+			  
+		# Wait for some active circuits to complete; report metrics when groups complete
+		ex.throttle_execution(metrics.finalize_group)  
+		
+	# Wait for all active circuits to complete; report metrics when groups complete
+	ex.finalize_execution(metrics.finalize_group)
+	   
+	##########
+	
+	# draw a sample circuit
+	kernel_draw()
+
+	# Plot metrics for all circuit sizes						 
+	metrics.plot_metrics(f"Benchmark Results - {benchmark_name} ({method}) - Qiskit")
 
 #######################
 # MAIN
 
 import argparse
 def get_args():
-    parser = argparse.ArgumentParser(description="Quantum Fourier Transform Benchmark")
-    parser.add_argument("--api", "-a", default=None, help="Programming API", type=str)
-    parser.add_argument("--target", "-t", default=None, help="Target Backend", type=str)
-    parser.add_argument("--backend_id", "-b", default=None, help="Backend Identifier", type=str)
-    parser.add_argument("--num_shots", "-s", default=100, help="Number of shots", type=int)
-    parser.add_argument("--num_qubits", "-n", default=0, help="Number of qubits", type=int)
-    parser.add_argument("--min_qubits", "-min", default=3, help="Minimum number of qubits", type=int)
-    parser.add_argument("--max_qubits", "-max", default=8, help="Maximum number of qubits", type=int)
-    parser.add_argument("--skip_qubits", "-k", default=1, help="Number of qubits to skip", type=int)
-    parser.add_argument("--max_circuits", "-c", default=3, help="Maximum circuit repetitions", type=int)  
-    parser.add_argument("--method", "-m", default=1, help="Algorithm Method", type=int)
-    parser.add_argument("--input_value", "-i", default=None, help="Fixed Input Value", type=int)
-    parser.add_argument("--nonoise", "-non", action="store_true", help="Use Noiseless Simulator")
-    parser.add_argument("--verbose", "-v", action="store_true", help="Verbose")
-    return parser.parse_args()
-    
+	parser = argparse.ArgumentParser(description="Quantum Fourier Transform Benchmark")
+	parser.add_argument("--api", "-a", default=None, help="Programming API", type=str)
+	parser.add_argument("--target", "-t", default=None, help="Target Backend", type=str)
+	parser.add_argument("--backend_id", "-b", default=None, help="Backend Identifier", type=str)
+	parser.add_argument("--num_shots", "-s", default=100, help="Number of shots", type=int)
+	parser.add_argument("--num_qubits", "-n", default=0, help="Number of qubits", type=int)
+	parser.add_argument("--min_qubits", "-min", default=3, help="Minimum number of qubits", type=int)
+	parser.add_argument("--max_qubits", "-max", default=8, help="Maximum number of qubits", type=int)
+	parser.add_argument("--skip_qubits", "-k", default=1, help="Number of qubits to skip", type=int)
+	parser.add_argument("--max_circuits", "-c", default=3, help="Maximum circuit repetitions", type=int)  
+	parser.add_argument("--method", "-m", default=1, help="Algorithm Method", type=int)
+	parser.add_argument("--input_value", "-i", default=None, help="Fixed Input Value", type=int)
+	parser.add_argument("--nonoise", "-non", action="store_true", help="Use Noiseless Simulator")
+	parser.add_argument("--verbose", "-v", action="store_true", help="Verbose")
+	return parser.parse_args()
+	
 # if main, execute method
 if __name__ == '__main__': 
-    args = get_args()
-    
-    # configure the QED-C Benchmark package for use with the given API
-    # (done here so we can set verbose for now)
-    QuantumFourierTransform, kernel_draw = qedc_benchmarks_init(args.api)
-    
-    # special argument handling
-    ex.verbose = args.verbose
-    verbose = args.verbose
-    
-    if args.num_qubits > 0: args.min_qubits = args.max_qubits = args.num_qubits
-    
-    # execute benchmark program
-    run(min_qubits=args.min_qubits, max_qubits=args.max_qubits,
-        skip_qubits=args.skip_qubits, max_circuits=args.max_circuits,
-        num_shots=args.num_shots,
-        method=args.method,
-        input_value=args.input_value,
-        backend_id=args.backend_id,
-        exec_options = {"noise_model" : None} if args.nonoise else {},
-        api=args.api
-        )
+	args = get_args()
+	
+	# configure the QED-C Benchmark package for use with the given API
+	# (done here so we can set verbose for now)
+	QuantumFourierTransform, kernel_draw = qedc_benchmarks_init(args.api)
+	
+	# special argument handling
+	ex.verbose = args.verbose
+	verbose = args.verbose
+	
+	if args.num_qubits > 0: args.min_qubits = args.max_qubits = args.num_qubits
+	
+	# execute benchmark program
+	run(min_qubits=args.min_qubits, max_qubits=args.max_qubits,
+		skip_qubits=args.skip_qubits, max_circuits=args.max_circuits,
+		num_shots=args.num_shots,
+		method=args.method,
+		input_value=args.input_value,
+		backend_id=args.backend_id,
+		exec_options = {"noise_model" : None} if args.nonoise else {},
+		api=args.api
+		)
    
diff --git a/quantum-fourier-transform/qiskit/qft_benchmark.py b/quantum-fourier-transform/qiskit/qft_benchmark.py
index fa5dae2e..75da3be0 100644
--- a/quantum-fourier-transform/qiskit/qft_benchmark.py
+++ b/quantum-fourier-transform/qiskit/qft_benchmark.py
@@ -131,7 +131,7 @@ def QuantumFourierTransform (num_qubits, secret_int, method=1):
 
 def qft_gate(input_size):
     global QFT_, num_gates, depth
-	# avoid name "qft" as workaround of https://github.com/Qiskit/qiskit/issues/13174
+    # avoid name "qft" as workaround of https://github.com/Qiskit/qiskit/issues/13174
     qr = QuantumRegister(input_size); qc = QuantumCircuit(qr, name="qft_")
     
     # Generate multiple groups of diminishing angle CRZs and H gate
diff --git a/shors/qiskit/shors_benchmark.py b/shors/qiskit/shors_benchmark.py
index fa25337e..d85c0dc4 100644
--- a/shors/qiskit/shors_benchmark.py
+++ b/shors/qiskit/shors_benchmark.py
@@ -338,7 +338,7 @@ def analyze_and_print_result(qc, result, num_qubits, order, num_shots, method):
 
 # Execute program with default parameters
 def run (min_qubits=3, max_circuits=1, max_qubits=18, num_shots=100, method = 1,
-        verbose=verbose, backend_id='qasm_simulator', provider_backend=None,
+        verbose=verbose, backend_id=None, provider_backend=None,
         hub="ibm-q", group="open", project="main", exec_options=None,
         context=None):