Variational Quantum Classification_example_values.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "48c619af",
   "metadata": {
    "tags": []
   },
   "source": [
    "\n",
    "# Variational Classification\n",
    "\n",
    "This is a example using qiskit to build a quantum classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fcef297a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
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      "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 21.3 is available.\n",
      "You should consider upgrading via the '/home/toto/anaconda3/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip install qiskit\n",
    "!pip install qiskit-machine-learning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "634b6fa4-fc62-4c7c-adcb-da14f8eae5c9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/toto/anaconda3/lib/python3.8/site-packages/qiskit/ml/__init__.py:40: DeprecationWarning: The package qiskit.ml is deprecated. It was moved/refactored to qiskit_machine_learning (pip install qiskit-machine-learning). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('ml', 'qiskit_machine_learning', 'qiskit-machine-learning')\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from qiskit import QuantumCircuit, transpile, Aer, IBMQ, execute, QuantumRegister, ClassicalRegister\n",
    "\n",
    "from qiskit.utils import QuantumInstance\n",
    "from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap\n",
    "from qiskit.algorithms.optimizers import COBYLA, L_BFGS_B\n",
    "from qiskit.circuit import Parameter, ParameterVector\n",
    "from qiskit_machine_learning.neural_networks import TwoLayerQNN, CircuitQNN\n",
    "from qiskit_machine_learning.algorithms.classifiers import NeuralNetworkClassifier, VQC\n",
    "from qiskit_machine_learning.algorithms.regressors import NeuralNetworkRegressor, VQR\n",
    "\n",
    "from typing import Union\n",
    "\n",
    "from qiskit_machine_learning.exceptions import QiskitMachineLearningError\n",
    "\n",
    "from IPython.display import clear_output\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from qiskit.ml.datasets import ad_hoc_data, sample_ad_hoc_data\n",
    "\n",
    "\n",
    "pi = np.pi\n",
    "\n",
    "seed = 10599\n",
    "#IBMQ.save_account('ff7b0e94064fe786ff6cf353d924e8e9503faef3971c6e5873220862bf14ce1989fbeb163597842d23f5e8a1d27ba7f9fb2f4af341da07da9c5bd109c109e76e')\n",
    "#provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0b71b085-0ef8-4f7e-b5be-e6dd20481867",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "Using the method ad_hoc_data to obtain a random dataset of 2 classes with 2 parameters."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "93293d25-4fc6-4538-96b1-707d073b630a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/toto/anaconda3/lib/python3.8/site-packages/qiskit/ml/datasets/ad_hoc.py:79: DeprecationWarning: The variable qiskit.aqua.aqua_globals is deprecated. It was moved/refactored to qiskit.utils.algorithm_globals (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  basis = aqua_globals.random.random((2 ** n, 2 ** n)) + \\\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "len_train = 40\n",
    "len_test = 10\n",
    "feature_dim = 2\n",
    "sample_total, training_input, test_input, class_labels = ad_hoc_data(\n",
    "    training_size=40,\n",
    "    test_size=10,\n",
    "    n=feature_dim,\n",
    "    gap=0.2,\n",
    "    plot_data=True\n",
    ")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0f06661b-4c69-4923-a082-3204e018cd77",
   "metadata": {},
   "source": [
    "### Trainset\n",
    "\n",
    "We need to design a preprocessing for the  training_input variable to the neccesary train sets: X_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ca1a1fa1-9fbc-49dc-a9ad-46fc5970ac34",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train = []\n",
    "y_train = []\n",
    "len_label = len(class_labels) \n",
    "index = 0 \n",
    "for i in class_labels:\n",
    "    y = [0.]*len_label\n",
    "    train_set = training_input[i]\n",
    "    X_train.extend(train_set)\n",
    "    for i in range(len(train_set)):\n",
    "        y[index] = 1.0\n",
    "        y_train.append(y)\n",
    "    index += 1\n",
    "X_train = np.asarray(X_train)\n",
    "y_train = np.asarray(y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9e88990-aa9f-4907-9f7a-97dc3ae0d585",
   "metadata": {},
   "source": [
    "Print the result of the preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "59d80d76-8a70-4a28-b325-83c49ee64c1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[4.27256601, 1.44513262],\n",
       "        [5.46637122, 3.51858377],\n",
       "        [2.70176968, 1.0681415 ],\n",
       "        [4.58672527, 1.50796447],\n",
       "        [3.51858377, 1.57079633],\n",
       "        [5.84336234, 6.09468975],\n",
       "        [4.1469023 , 5.46637122],\n",
       "        [5.2150438 , 1.50796447],\n",
       "        [1.19380521, 1.88495559],\n",
       "        [1.31946891, 4.96371639],\n",
       "        [0.75398224, 4.27256601],\n",
       "        [2.26194671, 3.76991118],\n",
       "        [1.0681415 , 1.25663706],\n",
       "        [5.90619419, 2.32477856],\n",
       "        [0.87964594, 1.94778745],\n",
       "        [5.02654825, 3.58141563],\n",
       "        [2.70176968, 0.56548668],\n",
       "        [5.96902604, 6.1575216 ],\n",
       "        [3.64424748, 2.45044227],\n",
       "        [2.89026524, 5.96902604],\n",
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       "        [2.26194671, 3.95840674],\n",
       "        [5.65486678, 6.09468975],\n",
       "        [4.46106157, 5.34070751],\n",
       "        [0.56548668, 6.22035345],\n",
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       "        [2.63893783, 6.03185789],\n",
       "        [6.1575216 , 6.09468975],\n",
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       "        [0.69115038, 6.22035345],\n",
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       "        [4.90088454, 0.50265482],\n",
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       "        [0.69115038, 5.65486678],\n",
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       "        [2.89026524, 2.76460154],\n",
       "        [4.39822972, 0.18849556],\n",
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       "        [5.27787566, 0.37699112],\n",
       "        [3.26725636, 2.19911486],\n",
       "        [4.1469023 , 3.0787608 ],\n",
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       "        [5.0893801 , 3.33008821],\n",
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       "        [2.51327412, 0.43982297],\n",
       "        [0.62831853, 5.52920307],\n",
       "        [1.0681415 , 3.33008821],\n",
       "        [2.26194671, 5.0893801 ],\n",
       "        [0.        , 1.57079633],\n",
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       "        [1.19380521, 0.18849556],\n",
       "        [1.13097336, 0.18849556],\n",
       "        [0.62831853, 3.39292007],\n",
       "        [4.27256601, 3.20442451],\n",
       "        [3.45575192, 4.58672527],\n",
       "        [3.26725636, 1.13097336],\n",
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       "        [1.50796447, 2.32477856],\n",
       "        [0.81681409, 5.15221195],\n",
       "        [4.83805269, 5.96902604],\n",
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       "        [3.89557489, 0.37699112],\n",
       "        [1.00530965, 6.22035345],\n",
       "        [5.27787566, 0.12566371],\n",
       "        [2.07345115, 5.34070751]]),\n",
       " array([[1., 0.],\n",
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       "        [0., 1.],\n",
       "        [0., 1.],\n",
       "        [0., 1.]]))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train, y_train"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "729c70b3-8662-4101-bf89-c1dea4072991",
   "metadata": {},
   "source": [
    "### Testset\n",
    "\n",
    "We need to design a preprocessing for the  test_input variable to the neccesary train sets: X_test, y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "18b0ff24-85a8-4d92-bbc3-98748b6cbe1a",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_test= []\n",
    "y_test = []\n",
    "index = 0 \n",
    "for i in class_labels:\n",
    "    y = [0.]*len_label\n",
    "    test_set = test_input[i]\n",
    "    X_test.extend(test_set)\n",
    "    for i in range(len(test_set)):\n",
    "        y[index] = 1.0\n",
    "        y_test.append(y)\n",
    "    index += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1c7b30df-b37f-46a7-ab42-b7696d3f75af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([array([2.70176968, 0.37699112]),\n",
       "  array([3.83274304, 4.71238898]),\n",
       "  array([3.70707933, 4.96371639]),\n",
       "  array([2.51327412, 3.45575192]),\n",
       "  array([5.59203492, 2.89026524]),\n",
       "  array([0.31415927, 2.19911486]),\n",
       "  array([1.25663706, 4.77522083]),\n",
       "  array([4.83805269, 1.63362818]),\n",
       "  array([4.64955713, 1.57079633]),\n",
       "  array([1.19380521, 4.96371639]),\n",
       "  array([1.38230077, 4.1469023 ]),\n",
       "  array([6.22035345, 1.44513262]),\n",
       "  array([4.20973416, 2.07345115]),\n",
       "  array([1.94778745, 5.02654825]),\n",
       "  array([4.77522083, 2.45044227]),\n",
       "  array([4.77522083, 2.07345115]),\n",
       "  array([5.15221195, 2.45044227]),\n",
       "  array([6.03185789, 1.75929189]),\n",
       "  array([6.22035345, 4.08407045]),\n",
       "  array([4.58672527, 2.38761042])],\n",
       " [[1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [1.0, 0.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0],\n",
       "  [0.0, 1.0]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test, y_test"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e391ef1-77df-4087-b129-a29e01eb00e6",
   "metadata": {},
   "source": [
    "### Design a method to plot the epoch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "29bff624-dffc-4836-a4d6-13b652615974",
   "metadata": {},
   "outputs": [],
   "source": [
    "# callback function that draws a live plot when the .fit() method is called\n",
    "def callback_graph(weights, obj_func_eval):\n",
    "    clear_output(wait=True)\n",
    "    objective_func_vals.append(obj_func_eval)\n",
    "    plt.title(\"Objective function value against iteration\")\n",
    "    plt.xlabel(\"Iteration\")\n",
    "    plt.ylabel(\"Objective function value\")\n",
    "    plt.plot(range(len(objective_func_vals)), objective_func_vals)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "945cd318-3938-4c86-8ebf-7aeec182b701",
   "metadata": {},
   "source": [
    "## QML part\n",
    "\n",
    "Design the maping to the variables of each instance of the train dataset and design the ansatz.\n",
    "\n",
    "### Mapping\n",
    "\n",
    "Using the ZZFeatureMap method with the feature dimension with 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a5ffb5ad-a27d-4326-95c5-33dfa546446c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 929.252x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_map = ZZFeatureMap(feature_dim)\n",
    "feature_map.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c8c6945-4d98-4ef0-bc73-a01a952cc690",
   "metadata": {},
   "source": [
    "### Ansatz\n",
    "\n",
    "The variational Quantum circuit, in qiskit exist the possibility to apply pre-defined ansatz like RealAmplitudes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "77852a30-cedc-493b-8dd4-80f56b48746e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 267.052x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansatz_c = RealAmplitudes(feature_dim, reps=1)\n",
    "ansatz_c.decompose().draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15953c87-76d0-4ed8-8cb6-a9b9ad945d27",
   "metadata": {},
   "source": [
    "This example we design own ansatz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eef7dc74-ca56-4306-a94d-d8c9e8bdec07",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ansatz(n, num_layers):\n",
    "    #Set the number of layers and qubits\n",
    "    #ParameterVectors are initialized with a string identifier and an integer specifying the vector length    \n",
    "    parameters = ParameterVector('θ', n*(num_layers+1))\n",
    "\n",
    "    circuit = QuantumCircuit(n,name='oracle')\n",
    "    \n",
    "    for i in range(n):\n",
    "        circuit.h(i)\n",
    "    \n",
    "    for layer in range(num_layers):\n",
    "        #Appending the parameterized Ry gates using parameters from the vector constructed above\n",
    "        for i in range(n):\n",
    "            circuit.ry(parameters[n*layer+i], i)\n",
    "\n",
    "        #Appending the entangling CNOT gates\n",
    "        for i in range(n-1):\n",
    "            circuit.cx(i,1)\n",
    "\n",
    "    #Appending one additional layer of parameterized Rz gates\n",
    "        for i in range(n):\n",
    "            circuit.rz(parameters[n*num_layers+i], i)\n",
    "    \n",
    "    for i in range(n):\n",
    "        circuit.h(i)\n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "1fa36de1-cad7-44a4-b32c-a218f4711b97",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_layers = 2\n",
    "ansatz_circuit = ansatz(feature_dim,num_layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "da4ae8df-2e06-499c-9c82-968b17d8ce3a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 568.052x144.48 with 1 Axes>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ansatz_circuit.draw(\"mpl\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c404628-be1a-4285-8fee-b2d39d5fe40d",
   "metadata": {},
   "source": [
    "## Simulation  part\n",
    "\n",
    "The quantum instance for  simulation a quantum computing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b55de307-74bc-481d-937b-e968bc758e13",
   "metadata": {},
   "outputs": [],
   "source": [
    "quantum_instance = QuantumInstance(Aer.get_backend('qasm_simulator'), shots=8192)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7743dfb1-8f7f-498e-8ab8-30a99de808b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# construct variational quantum classifier\n",
    "vqc = VQC(feature_map=feature_map,\n",
    "          ansatz=ansatz_circuit,\n",
    "          loss='cross_entropy',\n",
    "          optimizer=COBYLA(),\n",
    "          quantum_instance=quantum_instance,\n",
    "          callback=callback_graph)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "bdf9f2ab-68be-43a2-9afd-edf456294266",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "0.9375"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# create empty array for callback to store evaluations of the objective function\n",
    "objective_func_vals = []\n",
    "plt.rcParams[\"figure.figsize\"] = (12, 6)\n",
    "\n",
    "# fit classifier to data\n",
    "vqc.fit(X_train, y_train)\n",
    "\n",
    "# return to default figsize\n",
    "plt.rcParams[\"figure.figsize\"] = (6, 4)\n",
    "\n",
    "# score classifier\n",
    "vqc.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6cbff371-7677-4567-9291-d04135359fb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.85"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vqc.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caf345fa",
   "metadata": {},
   "source": [
    "# General quantum circuit\n",
    "\n",
    "For those kind of ways to simulate a quantum variational classifier we use the same quantum circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e004c02d",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_layers = 1\n",
    "params = np.random.random([(1+num_layers)*feature_dim])  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "7412763a",
   "metadata": {},
   "outputs": [],
   "source": [
    "def ansatz_m(n, num_layers,params):\n",
    "    #Set the number of layers and qubits\n",
    "    #ParameterVectors are initialized with a string identifier and an integer specifying the vector length    \n",
    "    parameters = ParameterVector('θ', n*(num_layers+1))\n",
    "    j = 0\n",
    "    params_dict = {}\n",
    "    circuit = QuantumCircuit(n)\n",
    "    \n",
    "    for i in range(n):\n",
    "        circuit.h(i)\n",
    "    \n",
    "    for layer in range(num_layers):\n",
    "        #Appending the parameterized Ry gates using parameters from the vector constructed above\n",
    "        for i in range(n):\n",
    "            circuit.ry(parameters[n*layer+i], i)\n",
    "\n",
    "        #Appending the entangling CNOT gates\n",
    "        for i in range(n-1):\n",
    "            circuit.cx(i,1)\n",
    "\n",
    "    #Appending one additional layer of parameterized Rz gates\n",
    "        for i in range(n):\n",
    "            circuit.rz(parameters[n*num_layers+i], i)\n",
    "    \n",
    "    for i in range(n):\n",
    "        circuit.h(i)\n",
    "    for p in parameters:\n",
    "        params_dict[p] = params[j] \n",
    "        j += 1\n",
    "    circuit = circuit.assign_parameters(parameters = params_dict)\n",
    "    return circuit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4701a54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import operator\n",
    "\n",
    "def sim_variational_classifier(params):\n",
    "    cost = 0\n",
    "    acc = 0\n",
    "    shots= 8192\n",
    "    for i in range(len_train*2):\n",
    "        qc = QuantumCircuit(feature_dim,feature_dim)\n",
    "        qc.append(feature_map,range(feature_dim))\n",
    "        j = 0\n",
    "        inputs_dict = {}\n",
    "        for index in feature_map.parameters:\n",
    "            inputs_dict[index] = X_train[i][j] \n",
    "            j += 1\n",
    "        qc = qc.assign_parameters(inputs_dict)\n",
    "        qc.append(ansatz_m(feature_dim,num_layers,params),range(feature_dim))\n",
    "        qc.measure(range(feature_dim),range(feature_dim))\n",
    "        result = execute(qc, Aer.get_backend('qasm_simulator')).result()                 \n",
    "        counts = result.get_counts(qc)\n",
    "        y_pred = max(counts.items(), key=operator.itemgetter(1))[0]\n",
    "        y_pred = [float(i) for i in y_pred]\n",
    "        if y_pred[0] == y_train[i][0] and y_pred[1] == y_train[i][1]:\n",
    "            acc += 1\n",
    "        else:\n",
    "            cost +=1\n",
    "    return cost/(len_train*2)\n",
    "num_parameters = 4\n",
    "sim_variational_classifier(params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2061b802",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cost\n",
      "0.65\n",
      "0.5625\n",
      "0.4375\n",
      "0.4125\n",
      "0.4\n",
      "0.4125\n",
      "0.4\n",
      "0.275\n",
      "0.2375\n",
      "0.25\n",
      "0.2375\n"
     ]
    }
   ],
   "source": [
    "print(\"cost\")\n",
    "print(sim_variational_classifier(params))\n",
    "\n",
    "for i in range(10):\n",
    "    optimizer = COBYLA(maxiter=20)\n",
    "    ret = optimizer.optimize(num_vars=len(params),objective_function=sim_variational_classifier, initial_point=params)\n",
    "    params = ret[0]\n",
    "    print(ret[1])\n",
    "    "
   ]
  }
 ],
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