experiments/ibmq_antiferro-1D_load-AD-bogota-no-optimize.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0d23564d-f4f7-45ae-9739-82901112ddd0",
   "metadata": {},
   "source": [
    "## Log  \n",
    "Tuesday 01.06.2021  \n",
    "All optimized parameters (VQE _and_ QAE parameters) from noisy simulation\n",
    "_just_ executing inference on real device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "62aac339",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "sys.path.insert(1, '..') # sets the import path to the parent folder\n",
    "\n",
    "import time\n",
    "import datetime\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "import qiskit\n",
    "from qiskit import *\n",
    "from qiskit.opflow import X,Z,I\n",
    "from qiskit.opflow.state_fns import StateFn, CircuitStateFn\n",
    "from qiskit.providers.aer import StatevectorSimulator, AerSimulator\n",
    "from qiskit.algorithms import VQE\n",
    "from qiskit.algorithms.optimizers import COBYLA, SLSQP, SPSA\n",
    "from qiskit.opflow import CircuitSampler\n",
    "\n",
    "\n",
    "from qiskit.ignis.mitigation.measurement import CompleteMeasFitter # you will need to pip install qiskit-ignis\n",
    "from qiskit.ignis.mitigation.measurement import complete_meas_cal\n",
    "\n",
    "from scipy import sparse\n",
    "import scipy.sparse.linalg.eigen.arpack as arp\n",
    "from modules.utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "19bd4fdf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "filename:  data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20 notebook name:  ibmq_antiferro-1D-load_bogota-no-optimize\n"
     ]
    }
   ],
   "source": [
    "anti = -1\n",
    "L = 5\n",
    "num_trash = 2\n",
    "\n",
    "name = \"ibmq_antiferro-1D-load_bogota-no-optimize\" # remove test at the end when running on a real device\n",
    "filename = \"data/noisy_VQE_maxiter-500_Ising_L5_anti_-1_20\" #\"data/noisy_VQE_maxiter-100_Ising_L5_anti_-1_20_recycle\"\n",
    "print(\"filename: \", filename, \"notebook name: \", name)\n",
    "\n",
    "# where to get the simulated thetas values from? needs to contain a thetas_mitigated array\n",
    "filename_simulated_thetas = 'data/ibmq_antiferro-1D-load_simu_thetas-loss-cost_run2.npz' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "08449fa6-e509-4362-b291-3fa1f5eadc2b",
   "metadata": {},
   "outputs": [],
   "source": [
    "load = False\n",
    "recompute = False # whether or not to recompute Magnetization, makes sense on device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3f2f5b8a-957e-403e-8bfe-f11757f2fb80",
   "metadata": {},
   "outputs": [],
   "source": [
    "IBMQ.load_account() # this then automatically loads your saved account\n",
    "provider = IBMQ.get_provider(hub='ibm-q-research')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84202903-386e-48e7-a523-8484f1f9b32d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ibmq_bogota\n"
     ]
    }
   ],
   "source": [
    "device = provider.backend.ibmq_bogota\n",
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1b3293e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "backend = device\n",
    "#backend = qiskit.providers.aer.AerSimulator.from_backend(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "53a699a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "coupling_map = device.configuration().coupling_map\n",
    "noise_model = qiskit.providers.aer.noise.NoiseModel.from_backend(device)\n",
    "basis_gates = noise_model.basis_gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "88b32b9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#aqua_globals.random_seed = seed\n",
    "qi = qiskit.utils.QuantumInstance(backend=backend, # , seed_simulator=seed, seed_transpiler=seed\n",
    "                         coupling_map=coupling_map, # noise_model=noise_model,\n",
    "                         measurement_error_mitigation_cls= CompleteMeasFitter, \n",
    "                         cals_matrix_refresh_period=30  #How often to refresh the calibration matrix in measurement mitigation. in minutes\n",
    "                                 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "63db279e-3a43-4fed-83f5-53bc7275e30a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cx', 'id', 'reset', 'rz', 'sx', 'x']"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "basis_gates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "df6df9a8-07eb-45c9-94cf-306d59bd1664",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 507.997x325.08 with 1 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Very important, at the moment poorly coded so it needs to come back to this instance all the time\n",
    "ansatz = qiskit.circuit.library.TwoLocal(L,rotation_blocks=\"ry\", entanglement_blocks='cz', entanglement=\"sca\", reps=1)\n",
    "ansatz.draw(\"mpl\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "5982b95b-37c3-4337-b9b6-5895953c7de4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1629.53x686.28 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansatz = qiskit.transpile(ansatz, backend)\n",
    "ansatz.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cb97445d-afaf-481b-afce-ad4a9981483a",
   "metadata": {},
   "source": [
    "## VQE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f448bff-685c-4cea-a2de-deb5edaf2255",
   "metadata": {},
   "source": [
    "## Magnetization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "4a2ad47f-5f6e-4f01-a18e-4c4d472151f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "L = 5\n",
    "num_trash = 2\n",
    "anti = -1\n",
    "\n",
    "VQE_params = np.load(filename + \".npz\", allow_pickle=True)\n",
    "pick = np.arange(0,len(VQE_params['gx_list']),3)\n",
    "gx_list = VQE_params['gx_list'][pick]\n",
    "gz_list = VQE_params['gz_list'][pick]\n",
    "opt_params = VQE_params['opt_params'][pick]\n",
    "Qmags = VQE_params[\"Qmag\"][pick]\n",
    "Qen = VQE_params[\"Qen\"][pick]\n",
    "Sen = VQE_params[\"Sen\"][pick]\n",
    "Smags = VQE_params[\"Smag\"][pick]\n",
    "\n",
    "gx_vals = np.unique(gx_list)\n",
    "gz_vals = np.unique(gz_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f998b5cb-d29a-48d8-b4b3-3ff429376834",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f6913b77-02ab-471c-bc18-c5cabb8534d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "if load:\n",
    "    temp = np.load(\"data/\" + name + \"executed_mags-Es.npz\",allow_pickle=True)\n",
    "    Qmags = temp[\"Qmags\"]\n",
    "    Qen = temp[\"Qen\"]\n",
    "    Sen = temp[\"Sen\"]\n",
    "    Smags = temp[\"Smags\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "11db28db-0cad-4cbc-ba97-bc449d881c86",
   "metadata": {},
   "outputs": [],
   "source": [
    "verbose=1\n",
    "if recompute:\n",
    "    mag = QMag(L,anti) #magnetization operator (Qiskit)\n",
    "    Smag = Mag(L,anti) #magnetization operator (numpy)\n",
    "\n",
    "    Qen_executed=np.zeros(len(opt_params), dtype=\"complex\")\n",
    "    Qmags_executed=np.zeros(len(opt_params), dtype=\"complex\")\n",
    "    for j in range(len(opt_params)):\n",
    "        t0 = datetime.datetime.now()\n",
    "        gx = gx_list[j]\n",
    "        gz = gz_list[j]\n",
    "        H = QHIsing(L, anti, np.float32(gx), np.float32(gz)) # build Hamiltonian Op\n",
    "        state = ansatz.assign_parameters(opt_params[j])\n",
    "        meas_outcome = ~StateFn(mag) @ StateFn(state)\n",
    "\n",
    "        Qmags_executed[j] = CircuitSampler(qi).convert(meas_outcome).eval() #https://quantumcomputing.stackexchange.com/questions/12080/evaluating-expectation-values-of-operators-in-qiskit\n",
    "        #e_outcome = ~StateFn(H) @ StateFn(state)\n",
    "        #Qen_executed[j] = CircuitSampler(qi).convert(e_outcome).eval()\n",
    "        \n",
    "        \n",
    "        init_state, E, ham = ising_groundstate(L, anti, np.float64(gx), np.float64(gz))\n",
    "        Sen[j] = E\n",
    "        Smags[j] = init_state.T.conj()@Smag@init_state #Magnetization with Numpy results\n",
    "        print(f\"{j+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {(datetime.datetime.now() - t0)}\")\n",
    "    np.savez(\"data/\" + name + \"executed_mags-Es.npz\",Qmags=Qmags_executed, Qen=Qen_executed, Sen=Sen, Smags=Smags) # for large parameter space takes quite a while because of the exact diagonalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "4889dcb2-41dc-4268-9a51-3a276a2c61fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/kkottmann/anaconda3/envs/qiskit_env/lib/python3.9/site-packages/numpy/core/_asarray.py:102: ComplexWarning: Casting complex values to real discards the imaginary part\n",
      "  return array(a, dtype, copy=False, order=order)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f0e68c50340>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2, figsize=(10,5))\n",
    "ax = axs[0]\n",
    "ax.plot(gx_list, Qmags,\"x--\", label=\"noisy VQE simu\")\n",
    "ax.plot(gx_list, Smags,\"x--\", label=\"ED\")\n",
    "ax.set_xscale(\"log\")\n",
    "if recompute:\n",
    "    ax.plot(gx_list, Qmags_executed,\"x--\", label=\"IBMQ\")\n",
    "ax.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "decb9c50",
   "metadata": {},
   "outputs": [],
   "source": [
    "##############################################################################\n",
    "### II - Training  ###########################################################\n",
    "##############################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7506d458-5270-4c00-8828-d594721754e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# linear entangler (as in scales linearly with trash qubits)\n",
    "def get_entangler_map(L, num_trash, i_permut=1, nums_trash=[1,2]):\n",
    "    result = []\n",
    "\n",
    "    nums_compressed = list(range(L))\n",
    "    for trashqubit in nums_trash:\n",
    "        nums_compressed.remove(trashqubit)\n",
    "    if nums_trash == None: #old way  \n",
    "        nums_compressed = list(range(L))[:L-num_trash]\n",
    "        nums_trash = list(range(L))[-num_trash:]\n",
    "    #print(nums, nums_compressed, nums_trash)\n",
    "    # combine all trash qubits with themselves\n",
    "    for trash_q in nums_trash[:-1]:\n",
    "        result.append((trash_q+1,trash_q))\n",
    "    # combine each of the trash qubits with every n-th\n",
    "    repeated = list(nums_trash) * (L-num_trash) # repeat the list of trash indices cyclicly\n",
    "    for i in range(L-num_trash):\n",
    "        result.append((repeated[i_permut + i], nums_compressed[i]))\n",
    "    return result\n",
    "\n",
    "def QAE_Ansatz(thetas, L, num_trash, insert_barriers=False, parametrized_gate = \"ry\", entangling_gate = \"cz\", nums_trash = [1,2]):\n",
    "    entanglement = [get_entangler_map(L,num_trash,i_permut) for i_permut in range(num_trash)]\n",
    "    if nums_trash == None:\n",
    "        nums_trash = list(range(L))[-num_trash:]\n",
    "    circ = qiskit.circuit.library.TwoLocal(L, \n",
    "                                           parametrized_gate,\n",
    "                                           entangling_gate,\n",
    "                                           entanglement,\n",
    "                                           reps=num_trash,\n",
    "                                           insert_barriers=insert_barriers,\n",
    "                                           skip_final_rotation_layer=True\n",
    "                                          ).assign_parameters(thetas[:-num_trash])\n",
    "    if insert_barriers: circ.barrier()\n",
    "\n",
    "    for i in nums_trash:\n",
    "        circ.ry(thetas[i], i)\n",
    "        #circ.ry(circuit.Parameter(f'θ{i}'), L-i-1)\n",
    "    return circ\n",
    "\n",
    "def prepare_circuit(thetas, L=6, num_trash=2, init_state=None, measurement=True, vqe=True):\n",
    "    qreg = QuantumRegister(L, 'q')\n",
    "    creg = ClassicalRegister(num_trash, 'c')\n",
    "    circ = QuantumCircuit(qreg, creg)\n",
    "    circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=False)#.assign_parameters(thetas) # difference to bind?\n",
    "    if measurement:\n",
    "        for i in range(num_trash):\n",
    "            circ.measure(qreg[L-i-1], creg[i])\n",
    "    if init_state is not None:\n",
    "        if vqe:\n",
    "            circ = ansatz.assign_parameters(init_state) + circ # needs to have ansatz defined somewhere in the script\n",
    "        else:\n",
    "            circ.initialize(init_state, qreg)\n",
    "    return circ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "42479c20-df4b-4672-a89d-29d75b0eec3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def feature_encoding(thetas, x):\n",
    "    \"\"\" thetas: parameters to be optimized, x: Ising model parameter (eg. field) \"\"\"\n",
    "    new_thetas = []\n",
    "    thetas = thetas.reshape((-1,2))\n",
    "    for theta in thetas:\n",
    "        new_thetas.append(theta[0] * x + theta[1])\n",
    "    return new_thetas"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0d3d5217-2e10-491c-b7b6-61df7ba2c468",
   "metadata": {},
   "source": [
    "### Execute circuit\n",
    "Circuit is executed on simulator and measurement outcomes on the trash qubits are stored"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "721bffce-1f39-4811-9f21-1f19d1b68d39",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calibrate_circuit(L, num_trash,shots=1000):\n",
    "    qreg = QuantumRegister(L, 'q')\n",
    "    # obtain calibration matrix\n",
    "    qubit_list = [L-i-1 for i in range(num_trash)] # only need to calibrate the trash qubits\n",
    "    circlabel = f'mcal_{datetime.datetime.now()}'\n",
    "    meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qreg, circlabel=circlabel)\n",
    "    cal_job = backend.run(meas_calibs, shots=shots) #, noise_model=noise_model\n",
    "    cal_results = cal_job.result()\n",
    "    meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel=circlabel)\n",
    "    print(circlabel, meas_fitter.cal_matrix)\n",
    "    return meas_fitter"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df4bdb13-a3a6-4e78-af30-36732d1dcdb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_circuit(thetas, L, num_trash, init_state, vqe=True, shots=100, meas_fitter = None):\n",
    "    circ = prepare_circuit(thetas, L, num_trash, init_state, vqe=vqe)\n",
    "    tcirc = qiskit.transpile(circ, backend)\n",
    "    # Execute the circuit \n",
    "    job_sim = backend.run(tcirc, shots=shots) # , seed_simulator=123, seed_transpiler=234 fix seed to make it reproducible\n",
    "    result = job_sim.result()\n",
    "    # Results without mitigation\n",
    "    counts = result.get_counts()\n",
    "    if meas_fitter != None:\n",
    "        # Get the filter object\n",
    "        meas_filter = meas_fitter.filter\n",
    "\n",
    "        # Results with mitigation\n",
    "        mitigated_results = meas_filter.apply(result)\n",
    "        counts = mitigated_results.get_counts(0)\n",
    "    return counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "25dcbc92-d901-4e75-b63c-ded35096db10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mcal_2021-06-01 21:07:39.851954 [[0.9   0.024 0.027 0.002]\n",
      " [0.019 0.969 0.    0.032]\n",
      " [0.08  0.    0.954 0.036]\n",
      " [0.001 0.007 0.019 0.93 ]]\n"
     ]
    }
   ],
   "source": [
    "meas_fitter = calibrate_circuit(L, num_trash)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f835d54e-fbb9-40cb-bdf2-c14888d203ac",
   "metadata": {},
   "source": [
    "### Optimize circuit\n",
    "Define cost function (averaged hamming distance of measurement outcomes) and minimze it using either scipy or qiskit optimizer modules (the latter is also based on scipy though)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "182296c0-15de-4e38-bd4e-ea07f3b6b944",
   "metadata": {},
   "outputs": [],
   "source": [
    "phis = opt_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f13f43d7-4ba0-47f7-9d60-9ccaf1809df8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# translate to Rikes naming\n",
    "\n",
    "gxs = gx_list\n",
    "gzs = gz_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "7d765c5c-bbc5-4bba-8d8f-fe9602a407de",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "def cost_function_single(thetas, L, num_trash, p, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):\n",
    "    \"\"\" Optimizes circuit \"\"\"\n",
    "    if vqe:\n",
    "        init_state = phis[p]\n",
    "    else:\n",
    "        J, gx, gz = p\n",
    "        init_state, _ = ising_groundstate(L, J, gx, gz)\n",
    "    if param_encoding: thetas = feature_encoding(thetas, x) \n",
    "    out = run_circuit(thetas, L, num_trash, init_state, vqe=vqe, shots=shots, meas_fitter=meas_fitter)\n",
    "    cost = out.get('11', 0)*2 + out.get('01', 0) + out.get('10', 0)\n",
    "    return cost/shots\n",
    "\n",
    "def cost_function(thetas, L, num_trash, ising_params, shots=1000, vqe=True, param_encoding=False, x=0, meas_fitter=None):\n",
    "    \"\"\" Optimizes circuit \"\"\"\n",
    "    cost = 0.\n",
    "    n_samples = len(ising_params)\n",
    "    for i, p in enumerate(ising_params):\n",
    "        if param_encoding: \n",
    "            cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, x[i], meas_fitter=meas_fitter)\n",
    "        else:\n",
    "            cost += cost_function_single(thetas, L, num_trash, p, shots, vqe, param_encoding, meas_fitter=meas_fitter)\n",
    "    return cost/n_samples\n",
    "\n",
    "def optimize(ising_params, L=6, num_trash=2, thetas=None, shots=1000, max_iter=400, vqe=True, param_encoding=False, x=0, pick_optimizer = None,\n",
    "            meas_fitter=None):\n",
    "    if thetas is None:\n",
    "        n_params = (2*L+2)*2 if param_encoding else (2*L+2)\n",
    "        thetas = np.random.uniform(0, 2*np.pi, n_params) # initial parameters without feature encoding\n",
    "        \n",
    "    print(\"Initial cost: {:.3f}\".format(cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x)))\n",
    "    \n",
    "    counts, values, accepted = [], [], []\n",
    "    def store_intermediate_result(eval_count, parameters, mean, std, ac):\n",
    "        # counts.append(eval_count)\n",
    "        values.append(mean)\n",
    "        accepted.append(ac)\n",
    "\n",
    "    # Initialize optimizer\n",
    "    if pick_optimizer == \"cobyla\":\n",
    "        optimizer = COBYLA(maxiter=max_iter, tol=0.0001)\n",
    "    if pick_optimizer == \"adam\" or pick_optimizer == \"ADAM\":\n",
    "        optimizer = qiskit.algorithms.optimizers.ADAM(maxiter=max_iter)\n",
    "    # optimizer = L_BFGS_B(maxfun=300, maxiter=max_iter)#, factr=10, iprint=- 1, epsilon=1e-08)\n",
    "    if pick_optimizer == \"spsa\" or pick_optimizer == None:\n",
    "        optimizer = SPSA(maxiter=max_iter,\n",
    "                         #blocking=True,\n",
    "                         callback=store_intermediate_result,\n",
    "                         learning_rate=0.3,\n",
    "                         perturbation=0.1\n",
    "                         ) # recommended from qiskit (first iteraction takes quite long)\n",
    "                           # to reduce time figure out optimal learning rate and perturbation in advance\n",
    "\n",
    "    start_time = time.time()\n",
    "    ret = optimizer.optimize(\n",
    "                            num_vars=len(thetas),\n",
    "                            objective_function=(lambda thetas: cost_function(thetas, L, num_trash, ising_params, shots, vqe, param_encoding, x, meas_fitter=meas_fitter)),\n",
    "                            initial_point=thetas\n",
    "                            )\n",
    "    print(\"Time: {:.5f} sec\".format(time.time()-start_time))\n",
    "    print(ret)\n",
    "    return ret[0], values, accepted\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9bc74323-6fa2-43ba-a377-2e983c625d53",
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_inference(thetas, shots=1000, L=5, meas_fitter=None):\n",
    "    points = 50\n",
    "    J = -1.0\n",
    "    x,y = np.meshgrid(gx_vals, gz_vals)\n",
    "    cost = np.zeros((len(gx_vals) * len(gz_vals)))\n",
    "    Smags = np.zeros((len(gx_vals) * len(gz_vals)))\n",
    "    shots = 1000\n",
    "    for i,p in enumerate(list(zip(gxs, gzs))):\n",
    "        t0 = datetime.datetime.now()\n",
    "        cost[i] = cost_function_single(thetas, L, num_trash, i, shots=shots, meas_fitter=meas_fitter)\n",
    "        if not i%verbose:\n",
    "            print(f\"{i+1} / {len(opt_params)}, gx = {gx:.2f}, gz = {gz:.2f}, time : {verbose*(datetime.datetime.now() - t0)}\")\n",
    "\n",
    "    cost = cost.reshape((len(gx_vals), len(gz_vals)))\n",
    "    return cost"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ae48f2f1-031f-4ba6-b23d-3b7d61fca1c2",
   "metadata": {},
   "source": [
    "## Run 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "f1c724ee-f589-4d12-b995-6f32ae8e8b94",
   "metadata": {},
   "outputs": [],
   "source": [
    "phys_params = [-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "3d7f2903-1513-403b-9b68-571ebb2551c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "thetas_opt_raw = np.load(filename_simulated_thetas, allow_pickle=True)[\"thetas_mitigated\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "abe164e7-4fbd-46fa-aea7-c38fa9f6fabb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-19-4e5192582d56>:44: DeprecationWarning: The QuantumCircuit.__iadd__() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  circ += QAE_Ansatz(thetas, L, num_trash, insert_barriers=False)#.assign_parameters(thetas) # difference to bind?\n",
      "/home/kkottmann/anaconda3/envs/qiskit_env/lib/python3.9/site-packages/qiskit/circuit/quantumcircuit.py:876: DeprecationWarning: The QuantumCircuit.extend() method is being deprecated. Use the compose() (potentially with the inplace=True argument) and tensor() methods which are more flexible w.r.t circuit register compatibility.\n",
      "  return self.extend(rhs)\n",
      "<ipython-input-19-4e5192582d56>:50: DeprecationWarning: The QuantumCircuit.__add__() method is being deprecated.Use the compose() method which is more flexible w.r.t circuit register compatibility.\n",
      "  circ = ansatz.assign_parameters(init_state) + circ # needs to have ansatz defined somewhere in the script\n",
      "/home/kkottmann/anaconda3/envs/qiskit_env/lib/python3.9/site-packages/qiskit/circuit/quantumcircuit.py:869: DeprecationWarning: The QuantumCircuit.combine() method is being deprecated. Use the compose() method which is more flexible w.r.t circuit register compatibility.\n",
      "  return self.combine(rhs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 7, gx = 0.01, gz = 0.00, time : 0:00:25.831521\n",
      "2 / 7, gx = 0.04, gz = 0.00, time : 0:00:25.189938\n",
      "3 / 7, gx = 0.18, gz = 0.00, time : 0:00:25.499119\n",
      "4 / 7, gx = 0.78, gz = 0.00, time : 0:00:25.267391\n",
      "5 / 7, gx = 3.36, gz = 0.00, time : 0:00:25.316789\n",
      "6 / 7, gx = 14.38, gz = 0.00, time : 0:00:35.329417\n",
      "7 / 7, gx = 61.58, gz = 0.00, time : 0:00:56.729071\n"
     ]
    }
   ],
   "source": [
    "# Inference\n",
    "cost_raw = np.zeros((len(gx_vals)))\n",
    "shots = 1000\n",
    "for i,p in enumerate(list(zip(gxs, gzs))):\n",
    "    t0 = datetime.datetime.now()\n",
    "    cost_raw[i] = cost_function_single(thetas_opt_raw, L, num_trash, i, shots=shots, meas_fitter=None) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training \"does\" something - result was: with random paremters just get noise, so yes, it \"does\" something\n",
    "    if not i%verbose:\n",
    "        print(f\"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e410bd67-c5c8-45b5-a643-33ca0fae5897",
   "metadata": {},
   "outputs": [],
   "source": [
    "thetas_opt_raw = thetas_opt_mitigated.copy()\n",
    "cost_raw = cost_mitigated.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "98376d3f-750b-4f63-88e7-09290ec76b4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "thetas_opt_mitigated = np.load(filename_simulated_thetas, allow_pickle=True)[\"thetas_mitigated\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "bc34451c-8ef9-4dc1-8969-640ae41735f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 / 7, gx = 0.01, gz = 0.00, time : 0:00:25.014824\n",
      "2 / 7, gx = 0.04, gz = 0.00, time : 0:00:25.438297\n",
      "3 / 7, gx = 0.18, gz = 0.00, time : 0:00:25.189859\n",
      "4 / 7, gx = 0.78, gz = 0.00, time : 0:00:25.296841\n",
      "5 / 7, gx = 3.36, gz = 0.00, time : 0:00:25.216731\n",
      "6 / 7, gx = 14.38, gz = 0.00, time : 0:00:24.454633\n",
      "7 / 7, gx = 61.58, gz = 0.00, time : 0:00:35.178562\n"
     ]
    }
   ],
   "source": [
    "# Inference\n",
    "cost_mitigated = np.zeros((len(gx_vals)))\n",
    "shots = 1000\n",
    "for i,p in enumerate(list(zip(gxs, gzs))):\n",
    "    t0 = datetime.datetime.now()\n",
    "    cost_mitigated[i] = cost_function_single(thetas_opt_mitigated, L, num_trash, i, shots=shots, meas_fitter=meas_fitter) # np.random.uniform(0, 2*np.pi, 2*L+2) random parameters to check if training \"does\" something - result was: with random paremters just get noise, so yes, it \"does\" something\n",
    "    if not i%verbose:\n",
    "        print(f\"{i+1} / {len(opt_params)}, gx = {p[0]:.2f}, gz = {p[1]:.2f}, time : {verbose*(datetime.datetime.now() - t0)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b07955f4-d868-4aea-b728-ceb9e79faa66",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez(\"data/\" + name + \"_thetas-loss-cost_run2\",\n",
    "         cost_mitigated=cost_mitigated, thetas_mitigated=thetas_opt_mitigated,\n",
    "         cost_raw=cost_raw, thetas_raw=thetas_opt_raw,\n",
    "        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "021fca74-5a4c-4e1b-bc24-28927d9bebac",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(ncols=2,figsize=(12,5))\n",
    "ax = axs[0]\n",
    "ax.set_title(\"Raw results\")\n",
    "cost = np.load(\"data/\" + name + \"_thetas-loss-cost_run2.npz\", allow_pickle=True)[\"cost_raw\"]\n",
    "ax.plot(gx_list, cost,\".:\", label=\"raw output\")\n",
    "ax.plot(gx_list, Qmags,\"x--\", color=\"tab:orange\", label=\"Qmag\")\n",
    "ax.set_xscale(\"log\")\n",
    "for p in phys_params:\n",
    "    ax.plot(gx_list[p],cost[p],\"o\",alpha=0.3,color=\"magenta\")\n",
    "    \n",
    "ax = axs[1]\n",
    "ax.set_title(\"Mitigated results\")\n",
    "cost = np.load(\"data/\" + name + \"_thetas-loss-cost_run2.npz\", allow_pickle=True)[\"cost_mitigated\"]\n",
    "ax.plot(gx_list, cost,\".:\", label=\"raw output\")\n",
    "ax.plot(gx_list, Qmags,\"x--\", color=\"tab:orange\", label=\"Qmag\")\n",
    "ax.set_xscale(\"log\")\n",
    "for p in phys_params:\n",
    "    ax.plot(gx_list[p],cost[p],\"o\",alpha=0.3,color=\"magenta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "af94c8d3-1c52-42b2-95ef-d312242585e6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=1,figsize=(6,5))\n",
    "ax.set_title(\"Mitigated results\")\n",
    "cost = np.load(\"data/\" + name + \"_thetas-loss-cost_run2.npz\", allow_pickle=True)[\"cost_raw\"]\n",
    "ax.plot(gx_list, cost,\".:\", label=\"raw output\")\n",
    "ax.plot(gx_list, abs(Qmags),\"x--\", color=\"tab:orange\", label=\"Qmag\")\n",
    "ax.set_xscale(\"log\")\n",
    "for p in phys_params:\n",
    "    ax.plot(gx_list[p],cost[p],\"o\",alpha=0.3,color=\"magenta\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af1bce84-bd86-42d5-946e-a210a1a8860d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}