Merge branch 'gh_test' into dev

.github/workflows/functional_tests.yaml

@@ -36,3 +36,26 @@ jobs:
     - name: Test with pytest
       run: |
         pytest -m "not skip"
+    - name: Install TorchQuantum
+      run: |
+        pip install --editable .
+    - name: Test Examples
+      run: |
+        python3 examples/qubit_rotation/qubit_rotation.py --epochs 1
+        python3 examples/vqe/vqe.py --epochs 1 --steps_per_epoch 1
+        python3 examples/train_unitary_prep/train_unitary_prep.py --epochs 1
+        python3 examples/train_state_prep/train_state_prep.py --epochs 1
+        python3 examples/superdense_coding/superdense_coding_torchquantum.py
+        python3 examples/regression/run_regression.py --epochs 1
+        python3 examples/param_shift_onchip_training/param_shift.py
+        python3 examples/mnist/mnist_2qubit_4class.py --epochs 1
+        python3 examples/hadamard_grad/circ.py
+        python3 examples/encoder_examples/encoder_8x2ry.py
+        python3 examples/converter_tq_qiskit/convert.py
+        python3 examples/amplitude_encoding_mnist/mnist_new.py --epochs 1
+        python3 examples/amplitude_encoding_mnist/mnist_example.py --epochs 1
+        python3 examples/PauliSumOp/pauli_sum_op.py
+        python3 examples/regression/new_run_regression.py --epochs 1
+        python3 examples/quanvolution/quanvolution_trainable_quantum_layer.py --epochs 1
+        python3 examples/grover/grover_example_sudoku.py
+        python3 examples/param_shift_onchip_training/param_shift.py

examples/grover/grover_example_sudoku.py

@@ -28,7 +28,7 @@
 """
 
 import torchquantum as tq
-from torchquantum.algorithms import Grover
+from torchquantum.algorithm import Grover
 
 
 # To simplify the process, we can compile this set of comparisons into a list of clauses for convenience.
@@ -90,4 +90,4 @@ def XOR(input0, input1, output):
 	print("b = ", key[1])
 	print("c = ", key[2])
 	print("d = ", key[3])
-	print("")
+	print("")

examples/optimal_control/optimal_control.py

@@ -41,7 +41,7 @@
         dtype=torch.complex64,
     )
 
-    pulse = tq.QuantumPulseDirect(n_steps=4, hamil=[[0, 1], [1, 0]])
+    pulse = tq.pulse.QuantumPulseDirect(n_steps=4, hamil=[[0, 1], [1, 0]])
 
     optimizer = optim.Adam(params=pulse.parameters(), lr=5e-3)
 

examples/optimal_control/optimal_control_gaussian.py

@@ -41,7 +41,7 @@
         dtype=torch.complex64,
     )
 
-    pulse = tq.QuantumPulseGaussian(hamil=[[0, 1], [1, 0]])
+    pulse = tq.pulse.QuantumPulseGaussian(hamil=[[0, 1], [1, 0]])
 
     optimizer = optim.Adam(params=pulse.parameters(), lr=5e-3)
 

examples/optimal_control/optimal_control_multi_qubit.py

@@ -43,9 +43,9 @@
         dtype=torch.complex64,
     )
 
-    pulse_q0 = tq.QuantumPulseDirect(n_steps=10, hamil=[[0, 1], [1, 0]])
-    pulse_q1 = tq.QuantumPulseDirect(n_steps=10, hamil=[[0, 1], [1, 0]])
-    pulse_q01 = tq.QuantumPulseDirect(
+    pulse_q0 = tq.pulse.QuantumPulseDirect(n_steps=10, hamil=[[0, 1], [1, 0]])
+    pulse_q1 = tq.pulse.QuantumPulseDirect(n_steps=10, hamil=[[0, 1], [1, 0]])
+    pulse_q01 = tq.pulse.QuantumPulseDirect(
         n_steps=10,
         hamil=[
             [1, 0, 0, 0],

examples/qubit_rotation/qubit_rotation.py

@@ -6,6 +6,7 @@
 import torchquantum as tq
 import torch
 from torchquantum.measurement import expval_joint_analytical
+import argparse
 
 
 class OptimizationModel(torch.nn.Module):
@@ -42,15 +43,14 @@ def train(model, device, optimizer):
 
 
 # main function to run the optimization
-def main():
+def main(n_epochs):
     seed = 0
     torch.manual_seed(seed)
 
     use_cuda = torch.cuda.is_available()
     device = torch.device("cuda" if use_cuda else "cpu")
 
     model = OptimizationModel()
-    n_epochs = 200
     optimizer = torch.optim.SGD(model.parameters(), lr=0.1)
 
     for epoch in range(1, n_epochs + 1):
@@ -65,4 +65,12 @@ def main():
 
 
 if __name__ == "__main__":
-    main()
+    parser = argparse.ArgumentParser(
+        prog="Qubit Rotation",
+        description="Specify Parameters for Qubit Rotation Optimization Example",
+    )
+    parser.add_argument(
+        "--epochs", type=int, default=200, help="number of training epochs"
+    )
+    args = parser.parse_args()
+    main(args.epochs)

examples/regression/new_run_regression.py

@@ -305,12 +305,11 @@ def main():
         model.set_qiskit_processor(processor_simulation)
         valid_test(dataflow, q_device, "test", model, device, qiskit=True)
 
+        # final valid
+        valid_test(dataflow, q_device, "valid", model, device, True)
     except:
         pass
 
-    # final valid
-    valid_test(dataflow, q_device, "valid", model, device, True)
-
 
 if __name__ == "__main__":
     main()

examples/save_load_example/save_load.py

@@ -143,7 +143,7 @@ def save_load3():
     # print(model.q_layer.rx0._parameters)
 
     traced_cell = torch.jit.trace(model, (x))
-    torch.jit.save(traced_cell, "model_trace.pth")
+    torch.jit.save(traced_cell, "model_trace.pt")
 
     loaded_trace = torch.jit.load("model_trace.pt")
     y2 = loaded_trace(x)

examples/vqe/new_simple_vqe.py

@@ -30,7 +30,7 @@
 from torchquantum.plugin import qiskit2tq_op_history
 
 if __name__ == "__main__":
-    hamil = Hamiltonian.from_file("./examples/simple_vqe/h2.txt")
+    hamil = Hamiltonian.from_file("./examples/vqe/h2.txt")
 
     ops = [
         {'name': 'u3', 'wires': 0, 'trainable': True},

torchquantum/__init__.py

@@ -38,6 +38,7 @@
 from .noise_model import *
 from .algorithm import *
 from .dataset import *
+from .pulse import *
 
 # here we check whether the Qiskit parameterization bug is fixed, if not, a
 # warning message will be printed

torchquantum/algorithm/__init__.py

@@ -25,3 +25,4 @@
 from .vqe import *
 from .hamiltonian import *
 from .qft import *
+from .grover import *

torchquantum/layer/layers.py

@@ -51,12 +51,14 @@
     "CXCXCXLayer",
     "SWAPSWAPLayer",
     "RXYZCXLayer0",
+    "U3CU3Layer0",
     "QFTLayer",
     "EntangleLinear",
     "EntanglePairwise",
     "EntangleFull",
     "EntangleCircular",
     "EntanglementLayer",
+    "SethLayer0",
 ]
 
 

torchquantum/pulse/__init__.py

@@ -25,4 +25,5 @@
 from .utils import *
 from .sesolve import sesolve
 from .mesolve import mesolve
+from .pulses import *
 # from .smesolve import smesolve