VQC.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "source": [
    "# Importing Libraries \r\n",
    "\r\n",
    "from qiskit import QuantumCircuit, QuantumRegister, ClassicalRegister\r\n",
    "from qiskit import Aer\r\n",
    "\r\n",
    "from qiskit import *\r\n",
    "import numpy as np\r\n",
    "from qiskit.visualization import plot_bloch_multivector, plot_histogram\r\n",
    "%matplotlib inline\r\n",
    "import matplotlib.pyplot as plt\r\n",
    "\r\n",
    "import time\r\n",
    "from qiskit.circuit.library import ZFeatureMap\r\n",
    "from qiskit.circuit import ParameterVector\r\n",
    "from qiskit.aqua.utils import split_dataset_to_data_and_labels, map_label_to_class_name\r\n",
    "from qiskit.aqua import QuantumInstance\r\n",
    "from qiskit.aqua.algorithms import VQC\r\n",
    "from qiskit.aqua.components.optimizers import COBYLA\r\n",
    "\r\n",
    "from qiskit.aqua.components.feature_maps import FeatureMap\r\n",
    "from qiskit.aqua.components.variational_forms import VariationalForm\r\n",
    "from qiskit.transpiler import PassManager\r\n",
    "from qiskit.transpiler.passes import Unroller\r\n",
    "\r\n",
    "import plotly.express as px\r\n",
    "import pandas as pd"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\ACER\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\aqua\\__init__.py:86: DeprecationWarning: The package qiskit.aqua is deprecated. It was moved/refactored to qiskit-terra For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "  warn_package('aqua', 'qiskit-terra')\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "source": [
    "data = np.loadtxt('' + \"challenge_dataset_4_9_dim3_training.csv\", delimiter=\",\")\r\n",
    "\r\n",
    "# extracting the first column which contains the labels\r\n",
    "data_labels = data[:, :1].reshape(data.shape[0],)\r\n",
    "# extracting all the columns but the first which are our features\r\n",
    "data_features = data[:, 1:]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "source": [
    "# creating a dataframe using pandas only for the purpose of plotting\r\n",
    "df = pd.DataFrame({'Component 0':data_features[:,0], 'Component 1':data_features[:,1], \r\n",
    "                   'Component 2':data_features[:,2], 'label':data_labels})\r\n",
    "\r\n",
    "fig = px.scatter_3d(df, x='Component 0', y='Component 1', z='Component 2', color='label')\r\n",
    "fig.show()"
   ],
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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   "cell_type": "code",
   "execution_count": 4,
   "source": [
    "# Data Preprocessing\r\n",
    "four_datapoints = []\r\n",
    "nine_datapoints = []\r\n",
    "for i in range(6000):\r\n",
    "    if int(data_labels[i]) == 4:                   # extracting zeros\r\n",
    "        four_datapoints.append(data_features[i])\r\n",
    "\r\n",
    "for i in range(6000):\r\n",
    "    if int(data_labels[i]) == 9:                   # extracting ones\r\n",
    "        nine_datapoints.append(data_features[i])\r\n",
    "        \r\n",
    "four_datapoints = np.array(four_datapoints)\r\n",
    "nine_datapoints = np.array(nine_datapoints)\r\n",
    "\r\n",
    "training_input = {'4':four_datapoints[:2000], '9':nine_datapoints[:2000]}\r\n",
    "testing_input = {'4':four_datapoints[2000:], '9':nine_datapoints[2000:]}"
   ],
   "outputs": [],
   "metadata": {}
  },
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   "cell_type": "code",
   "execution_count": 5,
   "source": [
    "# Building Quantum Feature Map\r\n",
    "def feature_map(): \r\n",
    "    feature_map = ZFeatureMap(feature_dimension=3, reps= 6)\r\n",
    "    return feature_map"
   ],
   "outputs": [],
   "metadata": {}
  },
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   "cell_type": "code",
   "execution_count": 6,
   "source": [
    "# Building variational circuit\r\n",
    "def variational_circuit():       \r\n",
    "    num_qubits = 3            \r\n",
    "    reps = 1              # number of times you'd want to repeat the circuit\r\n",
    "\r\n",
    "    x = ParameterVector('pvector', length=num_qubits)  # creating a list of Parameters\r\n",
    "    var_circuit = QuantumCircuit(num_qubits)\r\n",
    "    \r\n",
    "\r\n",
    "    # defining our parametric form\r\n",
    "    for _ in range(reps):\r\n",
    "        for i in range(num_qubits):\r\n",
    "            var_circuit.ry(x[i], i)\r\n",
    "        for i in range(num_qubits):\r\n",
    "            for j in range(i + 1, num_qubits):\r\n",
    "                var_circuit.cy(i, j)\r\n",
    "                var_circuit.u1(x[i] * x[j], j)\r\n",
    "                var_circuit.cy(i, j)\r\n",
    "    \r\n",
    "    return var_circuit"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "source": [
    "# Building an Optimizer\r\n",
    "def classical_optimizer(): \r\n",
    "    # creating an optimizer object\r\n",
    "    cls_opt = COBYLA(maxiter=500, tol=0.001)\r\n",
    "    return cls_opt"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "source": [
    "# Creating a callback\r\n",
    "def call_back_vqc(eval_count, var_params, eval_val, index):\r\n",
    "    print(\"eval_count: {}\".format(eval_count))\r\n",
    "    print(\"var_params: {}\".format(var_params))\r\n",
    "    print(\"eval_val: {}\".format(eval_val))\r\n",
    "    print(\"index: {}\".format(index))"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "source": [
    "# Optimization\r\n",
    "seed = 10598\r\n",
    "\r\n",
    "# setting the backend to qasm_simulator with the \"statevector\" method on.\r\n",
    "backend = Aer.get_backend('qasm_simulator')\r\n",
    "backend_options = {\"method\": \"statevector\"}\r\n",
    "\r\n",
    "# creating a quantum instance using the backend and backend options taken before\r\n",
    "quantum_instance = QuantumInstance(backend, shots=1024, seed_simulator=seed, seed_transpiler=seed, \r\n",
    "                                   backend_options=backend_options)\r\n",
    "\r\n",
    "# creating a VQC instance which you will be used for training\r\n",
    "vqc = VQC(optimizer=classical_optimizer(), \r\n",
    "          feature_map=feature_map(), \r\n",
    "          var_form=variational_circuit(), \r\n",
    "          callback=call_back_vqc, \r\n",
    "          training_dataset=training_input,     \r\n",
    "          test_dataset=testing_input)  "
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\ACER\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\aqua\\quantum_instance.py:135: DeprecationWarning:\n",
      "\n",
      "The class qiskit.aqua.QuantumInstance is deprecated. It was moved/refactored to qiskit.utils.QuantumInstance (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "\n",
      "C:\\Users\\ACER\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\aqua\\components\\optimizers\\optimizer.py:49: DeprecationWarning:\n",
      "\n",
      "The package qiskit.aqua.components.optimizers is deprecated. It was moved/refactored to qiskit.algorithms.optimizers (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "\n",
      "C:\\Users\\ACER\\AppData\\Local\\Temp/ipykernel_1188/1506599552.py:17: DeprecationWarning:\n",
      "\n",
      "The QuantumCircuit.u1 method is deprecated as of 0.16.0. It will be removed no earlier than 3 months after the release date. You should use the QuantumCircuit.p method instead, which acts identically.\n",
      "\n",
      "C:\\Users\\ACER\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\aqua\\algorithms\\classifiers\\vqc.py:96: DeprecationWarning:\n",
      "\n",
      "The package qiskit.aqua.algorithms.classifiers is deprecated. It was moved/refactored to qiskit_machine_learning.algorithms.classifiers (pip install qiskit-machine-learning). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "\n",
      "C:\\Users\\ACER\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\aqua\\algorithms\\vq_algorithm.py:70: DeprecationWarning:\n",
      "\n",
      "The class qiskit.aqua.algorithms.VQAlgorithm is deprecated. It was moved/refactored to qiskit.algorithms.VariationalAlgorithm (pip install qiskit-terra). For more information see <https://github.com/Qiskit/qiskit-aqua/blob/main/README.md#migration-guide>\n",
      "\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "source": [
    "start = time.process_time()\r\n",
    "\r\n",
    "result = vqc.run(quantum_instance)\r\n",
    "\r\n",
    "print(\"time taken: \")\r\n",
    "print(time.process_time() - start)\r\n",
    "\r\n",
    "print(\"testing success ratio: {}\".format(result['testing_accuracy']))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "C:\\Users\\ACER\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\aqua\\algorithms\\quantum_algorithm.py:46: DeprecationWarning:\n",
      "\n",
      "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",
      "\n",
      "C:\\Users\\ACER\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\qiskit\\ml\\__init__.py:40: DeprecationWarning:\n",
      "\n",
      "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",
      "\n"
     ]
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      "var_params: [-3.00539692  0.29586002  0.30256899]\n",
      "eval_val: 0.5682606317319174\n",
      "index: 80\n",
      "time taken: \n",
      "2041.59375\n",
      "testing success ratio: 0.742\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "source": [
    "o_params = repr(vqc.optimal_params)\r\n",
    "print(o_params)"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "array([-3.00539692,  0.29586002,  0.30256899])\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "source": [
    "def return_optimal_params():\r\n",
    "    # STORE THE OPTIMAL PARAMETERS AS AN ARRAY IN THE VARIABLE optimal_parameters \r\n",
    "    optimal_parameters = [-3.00539692,  0.29586002,  0.30256899]\r\n",
    "    return np.array(optimal_parameters)"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "source": [
    "# Testing using dummy dataset\r\n",
    "def grade(test_data, test_labels, feature_map, variational_form, optimal_params, find_circuit_cost=True, verbose=True):\r\n",
    "    seed = 10598\r\n",
    "    model_accuracy = None \r\n",
    "    circuit_cost=None \r\n",
    "    ans = None\r\n",
    "    unrolled_circuit = None\r\n",
    "    result_msg=''\r\n",
    "    data_dim = np.array(test_data).shape[1]\r\n",
    "    dataset_size = np.array(test_data).shape[0]\r\n",
    "    dummy_training_dataset=training_input = {'A':np.ones((2,data_dim)), 'B':np.ones((2, data_dim))}\r\n",
    "    \r\n",
    "    # converting 4's to 0's and 9's to 1's for checking \r\n",
    "    test_labels_transformed = np.where(test_labels==4, 0., 1.)\r\n",
    "    max_qubit_count = 6\r\n",
    "    max_circuit_cost = 2000\r\n",
    "    \r\n",
    "    if(not(feature_map is None and variational_form is None and optimal_params is None and test_data is None and test_labels is None)):\r\n",
    "        if(isinstance(feature_map, (QuantumCircuit, FeatureMap)) and \r\n",
    "            isinstance(variational_form, (QuantumCircuit, VariationalForm)) and \r\n",
    "            isinstance(test_data, np.ndarray) and\r\n",
    "            isinstance(test_labels, np.ndarray) and\r\n",
    "            isinstance(optimal_params, np.ndarray) and\r\n",
    "            (dataset_size == test_labels_transformed.shape[0])):\r\n",
    "            \r\n",
    "            dummy_optimizer = COBYLA()\r\n",
    "\r\n",
    "            # setting up the backend and creating a quantum instance\r\n",
    "            backend = Aer.get_backend('qasm_simulator')\r\n",
    "            backend_options = {\"method\": \"statevector\"}\r\n",
    "            quantum_instance = QuantumInstance(backend, \r\n",
    "                                            shots=2000, \r\n",
    "                                            seed_simulator=seed, \r\n",
    "                                            seed_transpiler=seed, \r\n",
    "                                            backend_options=backend_options)\r\n",
    "\r\n",
    "            # creating a VQC instance and running the VQC.predict method to get the accuracy of the model \r\n",
    "            vqc = VQC(optimizer=dummy_optimizer, \r\n",
    "                    feature_map=feature_map, \r\n",
    "                    var_form=variational_form, \r\n",
    "                    training_dataset=dummy_training_dataset)\r\n",
    "            \r\n",
    "            \r\n",
    "            pass_ = Unroller(['u3', 'cx'])\r\n",
    "            pm = PassManager(pass_)\r\n",
    "            # constructing circuit with first datapoint\r\n",
    "            circuit = vqc.construct_circuit(data[0], optimal_params)\r\n",
    "            ans = vqc.predict(test_data, quantum_instance=quantum_instance, params=np.array(optimal_params))\r\n",
    "            model_accuracy = np.sum(np.equal(test_labels_transformed, ans[1]))/len(ans[1])\r\n",
    "\r\n",
    "\r\n",
    "    return model_accuracy, ans, circuit"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "source": [
    "grading_dataset_size=2000    \r\n",
    "grading_features = data_features[-grading_dataset_size:]\r\n",
    "grading_labels = data_labels[-grading_dataset_size:]"
   ],
   "outputs": [],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "source": [
    "start = time.process_time()\r\n",
    "\r\n",
    "accuracy, ans, full_circuit  =  grade(test_data=grading_features, \r\n",
    "                                        test_labels=grading_labels, \r\n",
    "                                        feature_map=feature_map(), \r\n",
    "                                        variational_form=variational_circuit(), \r\n",
    "                                        optimal_params=return_optimal_params())\r\n",
    "\r\n",
    "print(\"time taken: {} seconds\".format(time.process_time() - start))"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "time taken: 16.4375 seconds\n"
     ]
    }
   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "source": [
    "print(\"Accuracy of the model: {}\".format(accuracy))\r\n",
    "print(\"The complete unrolled circuit: \")\r\n",
    "full_circuit.draw()"
   ],
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Accuracy of the model: 0.783\n",
      "The complete unrolled circuit: \n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "     ┌─────────────────┐┌─────────────────┐\n",
       "q_0: ┤0                ├┤0                ├\n",
       "     │                 ││                 │\n",
       "q_1: ┤1 ZFeatureMap-15 ├┤1 circuit-326547 ├\n",
       "     │                 ││                 │\n",
       "q_2: ┤2                ├┤2                ├\n",
       "     └─────────────────┘└─────────────────┘\n",
       "c: 3/══════════════════════════════════════\n",
       "                                           "
      ],
      "text/html": [
       "<pre style=\"word-wrap: normal;white-space: pre;background: #fff0;line-height: 1.1;font-family: &quot;Courier New&quot;,Courier,monospace\">     ┌─────────────────┐┌─────────────────┐\n",
       "q_0: ┤0                ├┤0                ├\n",
       "     │                 ││                 │\n",
       "q_1: ┤1 ZFeatureMap-15 ├┤1 circuit-326547 ├\n",
       "     │                 ││                 │\n",
       "q_2: ┤2                ├┤2                ├\n",
       "     └─────────────────┘└─────────────────┘\n",
       "c: 3/══════════════════════════════════════\n",
       "                                           </pre>"
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     },
     "metadata": {},
     "execution_count": 17
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   ],
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "source": [],
   "outputs": [],
   "metadata": {}
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