VQC_qiskidemo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9f2db66b-6c79-4eef-8467-b2564314ddb5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# Importing standard Qiskit libraries\n",
    "from qiskit import QuantumCircuit, transpile, BasicAer, IBMQ\n",
    "from qiskit.tools.jupyter import *\n",
    "from qiskit.visualization import *\n",
    "from ibm_quantum_widgets import *\n",
    "from qiskit.providers.aer import QasmSimulator\n",
    "\n",
    "# Loading your IBM Quantum account(s)\n",
    "provider = IBMQ.load_account()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3284f96e-f635-464f-9578-0ff89290175c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQSimulator('ibmq_qasm_simulator') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_armonk') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_montreal') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_toronto') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_bogota') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_kolkata') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_mumbai') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_lima') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_belem') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_quito') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_guadalupe') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_brooklyn') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_jakarta') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibmq_manila') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibm_hanoi') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibm_lagos') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibm_cairo') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibm_auckland') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibm_perth') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>,\n",
       " <IBMQBackend('ibm_washington') from IBMQ(hub='ibm-q-internal', group='deployed', project='default')>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#provider = IBMQ.get_provider(hub='ibm-q-internal', group='deployed', project='default')\n",
    "#provider.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "986795a8-304c-4452-b918-2d6d94561dbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<IBMQBackend('ibmq_manila') from IBMQ(hub='ibm-q-community', group='iisc-hackathon', project='main')>]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# get the least-busy backend at IBM and run the quantum circuit there\n",
    "from qiskit.providers.ibmq import least_busy\n",
    "from qiskit.tools.monitor import job_monitor\n",
    "#backend = least_busy(provider.backends(filters=lambda b: b.configuration().n_qubits >= 2 and b.status().operational==True and not b.configuration().simulator))\n",
    "#print(backend)\n",
    "\n",
    "provider = IBMQ.get_provider(hub='ibm-q-community', group='iisc-hackathon', project='main')\n",
    "provider.backends()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "4f934c23-69c7-49aa-b0d9-5c6759cd3d4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit import BasicAer\n",
    "from qiskit.utils import QuantumInstance, algorithm_globals\n",
    "from qiskit.algorithms.optimizers import COBYLA\n",
    "from qiskit.circuit.library import TwoLocal\n",
    "from qiskit_machine_learning.algorithms import VQC\n",
    "from qiskit_machine_learning.datasets import wine\n",
    "from qiskit_machine_learning.circuit.library import RawFeatureVector\n",
    "from qiskit.circuit.library import RealAmplitudes, ZZFeatureMap, ZFeatureMap, NLocal\n",
    "from qiskit import QuantumCircuit\n",
    "from qiskit.circuit import Parameter\n",
    "\n",
    "from sklearn.metrics import classification_report, confusion_matrix\n",
    "from time import time\n",
    "seed = 10599"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "3e8b66d5-9c05-4d88-90e0-1d110cac2fb6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "statevector_simulator\n"
     ]
    }
   ],
   "source": [
    "simulator = BasicAer.get_backend('statevector_simulator')\n",
    "print(simulator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "fb49ae26-9bf8-425a-bb7d-adadbd6f96df",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2.63893783 4.33539786]\n",
      " [4.0212386  5.84336234]\n",
      " [0.62831853 5.96902604]\n",
      " [4.71238898 5.46637122]\n",
      " [2.136283   3.95840674]\n",
      " [4.0212386  5.52920307]\n",
      " [6.09468975 3.45575192]\n",
      " [3.39292007 5.59203492]\n",
      " [6.22035345 3.95840674]\n",
      " [5.15221195 4.20973416]\n",
      " [4.83805269 1.00530965]\n",
      " [0.18849556 0.31415927]\n",
      " [1.69646003 1.0681415 ]\n",
      " [1.69646003 0.9424778 ]\n",
      " [1.00530965 4.96371639]\n",
      " [1.75929189 1.00530965]\n",
      " [5.59203492 0.87964594]\n",
      " [1.82212374 1.31946891]\n",
      " [2.51327412 4.20973416]\n",
      " [1.13097336 5.90619419]\n",
      " [5.52920307 5.52920307]\n",
      " [3.76991118 0.31415927]\n",
      " [1.88495559 6.03185789]\n",
      " [5.96902604 0.9424778 ]\n",
      " [3.33008821 4.0212386 ]\n",
      " [4.0212386  1.50796447]\n",
      " [6.22035345 0.        ]\n",
      " [0.69115038 3.20442451]\n",
      " [5.0893801  3.95840674]\n",
      " [1.57079633 6.03185789]\n",
      " [3.0787608  1.31946891]\n",
      " [0.62831853 0.12566371]\n",
      " [5.0893801  2.26194671]\n",
      " [3.39292007 4.08407045]\n",
      " [3.01592895 2.63893783]\n",
      " [5.34070751 5.78053048]\n",
      " [4.96371639 0.56548668]\n",
      " [4.46106157 3.83274304]\n",
      " [5.59203492 4.33539786]\n",
      " [3.0787608  4.0212386 ]]\n",
      "[[1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]]\n",
      "40\n",
      "[[0.37699112 3.58141563]\n",
      " [4.52389342 5.34070751]\n",
      " [4.90088454 1.63362818]\n",
      " [1.82212374 3.58141563]\n",
      " [4.46106157 4.96371639]\n",
      " [2.136283   0.81681409]\n",
      " [5.02654825 5.0893801 ]\n",
      " [5.84336234 3.51858377]\n",
      " [5.2150438  4.1469023 ]\n",
      " [3.95840674 5.65486678]\n",
      " [5.34070751 0.31415927]\n",
      " [2.38761042 3.33008821]\n",
      " [5.52920307 2.95309709]\n",
      " [2.136283   2.95309709]\n",
      " [2.89026524 4.08407045]\n",
      " [1.88495559 4.46106157]\n",
      " [5.78053048 2.63893783]\n",
      " [3.64424748 4.08407045]\n",
      " [5.59203492 1.82212374]\n",
      " [0.06283185 0.9424778 ]]\n",
      "[[1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [1. 0.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]\n",
      " [0. 1.]]\n",
      "20\n"
     ]
    }
   ],
   "source": [
    "from qiskit_machine_learning.datasets import ad_hoc_data\n",
    "\n",
    "feature_dim = 2\n",
    "train_data, train_labels, test_data, test_labels = ad_hoc_data(\n",
    "    training_size=20,\n",
    "    test_size=10,\n",
    "    n=feature_dim,\n",
    "    gap=0.3,\n",
    "    plot_data=True\n",
    ")\n",
    "print(train_data)\n",
    "print(train_labels)\n",
    "print(len(train_data))\n",
    "print(test_data)\n",
    "print(test_labels)\n",
    "print(len(test_data))\n",
    "\n",
    "#extra_test_data = ad_hoc_data(sample_total, 10, n=feature_dim)\n",
    "#datapoints, class_to_label = split_dataset_to_data_and_labels(extra_test_data)\n",
    "#print(class_to_label)"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "id": "b99e6278-ce85-48d8-b163-76b658ab6172",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.8/site-packages/qiskit/utils/run_circuits.py:745: UserWarning: seed_simulator is not a recognized runtime option and may be ignored by the backend.\n",
      "  return backend.run(circuits, **run_kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "training time for VQC :  29.651851892471313\n"
     ]
    }
   ],
   "source": [
    "feature_map = ZZFeatureMap(feature_dimension=feature_dim, reps=1)\n",
    "ansatz = TwoLocal(feature_dim, ['ry', 'rz'], 'cz', reps=1)\n",
    "vqc = VQC(feature_map=feature_map,\n",
    "                 ansatz=ansatz,\n",
    "                 optimizer=COBYLA(maxiter=1),\n",
    "                 quantum_instance=QuantumInstance(backend=backend,shots=1,\n",
    "                                                 seed_simulator=seed,\n",
    "                                                 seed_transpiler=seed)\n",
    "         )\n",
    "start_time = time()\n",
    "vqc.fit(train_data, train_labels)\n",
    "end_time = time()\n",
    "duration = end_time - start_time\n",
    "print(\"training time for VQC : \", duration)\n",
    "result = vqc.predict(test_data)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "21ccaa3b-2d66-41cd-9c8b-6c9b59e58ac2",
   "metadata": {},
   "outputs": [],
   "source": [
    "result2=np.delete(result,1,1)\n",
    "test_labels2=np.delete(test_labels,1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "2ac60573-7cc8-4779-b22e-877337034d96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.70      0.82        10\n",
      "           1       0.77      1.00      0.87        10\n",
      "\n",
      "   micro avg       0.85      0.85      0.85        20\n",
      "   macro avg       0.88      0.85      0.85        20\n",
      "weighted avg       0.88      0.85      0.85        20\n",
      " samples avg       0.85      0.85      0.85        20\n",
      "\n",
      "[[10  0]\n",
      " [ 3  7]]\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(test_labels,result))\n",
    "print(confusion_matrix(test_labels2,result2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c8e7fee-d1c2-417d-88da-7838b87a71cf",
   "metadata": {},
   "outputs": [],
   "source": [
    "more_test_data = sample_ad_hoc_data(sample_total, 10, n=feature_dim)\n",
    "more_datapoints, _ = split_dataset_to_data_and_labels(more_test_data)\n",
    "\n",
    "predicted_probabilities, predicted_labels = vqc.predict(datapoints[0])\n",
    "\n",
    "print('Prediction from more_datapoints set:')\n",
    "print(f'  ground truth: {map_label_to_class_name(more_datapoints[1], vqc.label_to_class)}')\n",
    "print(f'  prediction:   {map_label_to_class_name(predicted_labels, vqc.label_to_class)}')\n",
    "print(f'  success rate: {100*np.count_nonzero(predi"
   ]
  }
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