{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2438979d",
   "metadata": {},
   "source": [
    "# Quantum information and quantum computing - Problem set 11\n",
    "\n",
    "### _Problem 1_ : Repetition quantum error correction code"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aef31181",
   "metadata": {},
   "source": [
    "In this notebook we will explore a simple type of error correction on quantum computers that takes advantage of repetitions. In this case, we are going to use $n=3$ physical qubits in order to represent the information contained in a single, logical qubit.\n",
    "\n",
    "We do it by repeating the information contained in a single qubit on three qubits. Be careful that repeating does not mean \"cloning\". In fact, given a state\n",
    "\n",
    "\\begin{equation}\n",
    "|\\psi \\rangle = \\alpha |0\\rangle + \\beta |1 \\rangle\n",
    "\\end{equation}\n",
    "\n",
    "the repeated state is \n",
    "\n",
    "\\begin{equation}\n",
    "\\alpha |000 \\rangle + \\beta |111 \\rangle\n",
    "\\end{equation}\n",
    "\n",
    "and not $|\\psi \\rangle \\otimes |\\psi \\rangle \\otimes |\\psi \\rangle$, which is in general prevented by the no-cloning theorem.\n",
    "\n",
    "Therefore what we want to check is if the state is repeated in each of the three qubits.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b2c4b92f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First, import all the useful methods\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt   \n",
    "import math\n",
    "\n",
    "from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister\n",
    "from qiskit_aer import QasmSimulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84165428",
   "metadata": {},
   "source": [
    "1) The first thing we want to create is a circuit that, using ancilla qubits , can spot errors by measuring the error syndromes. For $n=3$ we will need $m=2$ ancilla qubits, together with the classical registers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae00ba9f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/clemensgiuliani/epfl/teaching/qc24/venv_qiskit/lib/python3.12/site-packages/qiskit/visualization/circuit/matplotlib.py:269: UserWarning: Style JSON file 'iqx.json' not found in any of these locations: /Users/clemensgiuliani/epfl/teaching/qc24/venv_qiskit/lib/python3.12/site-packages/qiskit/visualization/circuit/styles/iqx.json, iqx.json. Will use default style.\n",
      "  self._style, def_font_ratio = load_style(self._style)\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 781.465x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create the circuits\n",
    "\n",
    "cq = QuantumRegister(3,'code')\n",
    "aq = QuantumRegister(2,'ancilla') \n",
    "sb = ClassicalRegister(2)\n",
    "\n",
    "qc = QuantumCircuit(cq,aq,sb)\n",
    "\n",
    "# Inegrate the CNOT to spot errors\n",
    "\n",
    "qc.cx(cq[0],aq[0])\n",
    "qc.cx(cq[1],aq[0])\n",
    "\n",
    "qc.cx(cq[1],aq[1])\n",
    "qc.cx(cq[2],aq[1])\n",
    "\n",
    "qc.barrier()\n",
    "# Measure\n",
    "\n",
    "qc.measure(aq[0],sb[0])\n",
    "qc.measure(aq[1],sb[1])\n",
    "\n",
    "# Print the circuit\n",
    "qc.draw('mpl', style={'name': 'iqx'})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c0c51ac",
   "metadata": {},
   "source": [
    "In this way, if the outcome of the measure is $00$, we know that no error occurred. If it's $01$ we know the first qubit was flipped, $10$ means the third qubit was fliped and, finally, $11$ means that the second qubit was flipped."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fb25a5a5",
   "metadata": {},
   "source": [
    "2) Now we implement the circuit on the `qasm_simulator` and see if it recognize the flips correctly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ee42fe3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Call the backend\n",
    "\n",
    "backend = QasmSimulator()\n",
    "shots = 2048"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3324f212",
   "metadata": {},
   "source": [
    "As a first thing, we are going to see what appens if we do nothing to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05e1f065",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00': 2048}\n"
     ]
    }
   ],
   "source": [
    "results = backend.run(qc, shots=shots).result()\n",
    "answer = results.get_counts()\n",
    "\n",
    "plt.suptitle(\"Syndrome measurement\")\n",
    "plt.bar(answer.keys(), answer.values(), color='royalblue')\n",
    "plt.show()\n",
    "\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "125fce28",
   "metadata": {},
   "source": [
    "Now flip each of the qubits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "577e3cc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01': 2048}\n"
     ]
    }
   ],
   "source": [
    "# First qubit flipped\n",
    "\n",
    "init = QuantumRegister(3,\"code\")\n",
    "initc = QuantumCircuit(init, QuantumRegister(2), ClassicalRegister(2))\n",
    "initc.x(init[0])\n",
    "\n",
    "\n",
    "\n",
    "# Execute\n",
    "\n",
    "results = backend.run(initc.compose(qc), shots=shots).result()\n",
    "answer = results.get_counts()\n",
    "\n",
    "plt.suptitle(\"Syndrome measurement for flip on first qubit\")\n",
    "plt.bar(answer.keys(), answer.values(), color='royalblue')\n",
    "plt.show()\n",
    "\n",
    "print(answer)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2a2d4963",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'11': 2048}\n"
     ]
    }
   ],
   "source": [
    "# Second qubit flipped\n",
    "\n",
    "init = QuantumRegister(3,\"code\")\n",
    "initc = QuantumCircuit(init, QuantumRegister(2), ClassicalRegister(2))\n",
    "initc.x(init[1])\n",
    "\n",
    "\n",
    "\n",
    "# Execute\n",
    "\n",
    "results = backend.run(initc.compose(qc), shots=shots).result()\n",
    "answer = results.get_counts()\n",
    "\n",
    "plt.suptitle(\"Syndrome measurement for flip on second qubit\")\n",
    "plt.bar(answer.keys(), answer.values(), color='royalblue')\n",
    "plt.show()\n",
    "\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9464960b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'10': 2048}\n"
     ]
    }
   ],
   "source": [
    "# Third qubit flipped\n",
    "\n",
    "init = QuantumRegister(3,\"code\")\n",
    "initc = QuantumCircuit(init, QuantumRegister(2), ClassicalRegister(2))\n",
    "initc.x(init[2])\n",
    "\n",
    "\n",
    "\n",
    "# Execute\n",
    "\n",
    "results = backend.run(initc.compose(qc), shots=shots).result()\n",
    "answer = results.get_counts()\n",
    "\n",
    "plt.suptitle(\"Syndrome measurement for flip on third qubit\")\n",
    "plt.bar(answer.keys(), answer.values(), color='royalblue')\n",
    "plt.show()\n",
    "\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c00426e",
   "metadata": {},
   "source": [
    "So we have that our circuit recognize correctly the bit flips.\n",
    "\n",
    "3) Now we are going to import a noise model from a real device in order to see how effective is the QECC we created to recognize errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "829769d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.providers.fake_provider import GenericBackendV2\n",
    "from qiskit_aer.noise import NoiseModel\n",
    "\n",
    "simulator = GenericBackendV2(num_qubits=5)\n",
    "noise_model = NoiseModel.from_backend(simulator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "527822aa",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the circuit in |111>\n",
    "\n",
    "init = QuantumRegister(3,\"code\")\n",
    "initc = QuantumCircuit(init, QuantumRegister(2), ClassicalRegister(2))\n",
    "initc.x(init[0])\n",
    "initc.x(init[1])\n",
    "initc.x(init[2])\n",
    "\n",
    "# and execute the circuit with noise model\n",
    "\n",
    "\n",
    "results = simulator.run(initc.compose(qc), backend=simulator, shots=shots,noise_model = noise_model).result()\n",
    "answer = results.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "27009e3d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01': 29, '11': 3, '10': 28, '00': 1988}\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "\n",
    "plt.suptitle(\"Syndrome measurement with hardware noise model\")\n",
    "plt.bar(answer.keys(), answer.values(), color='royalblue')\n",
    "plt.show()\n",
    "\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39782498",
   "metadata": {},
   "source": [
    "As we can see, more than the $97\\%$ of the shots didn't get any bit-flip errors, sometimes the first and the third qubits flipped. \n",
    "\n",
    "Remember that we are not detecting other types of errors, like the phase error."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46c40553",
   "metadata": {},
   "source": [
    "4) Now we initialize the circuit with a generic initial state, as an example we create the state $\\alpha|0\\rangle+ \\beta|1\\rangle$ using a generic unitary on the first qubit and then we encode it in the repetition code "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d12ff10d",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/clemensgiuliani/epfl/teaching/qc24/venv_qiskit/lib/python3.12/site-packages/qiskit/visualization/circuit/matplotlib.py:269: UserWarning: Style JSON file 'iqx.json' not found in any of these locations: /Users/clemensgiuliani/epfl/teaching/qc24/venv_qiskit/lib/python3.12/site-packages/qiskit/visualization/circuit/styles/iqx.json, iqx.json. Will use default style.\n",
      "  self._style, def_font_ratio = load_style(self._style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1174.89x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "init = QuantumRegister(3,\"code\")\n",
    "initc = QuantumCircuit(init, QuantumRegister(2), ClassicalRegister(2))\n",
    "initc.u(0.1,0.1,0.1,init[0])  ## Use some parameters \\theta, \\phi, \\lambda of your choice\n",
    "initc.cx(init[0],init[1])\n",
    "initc.cx(init[1],init[2])\n",
    "initc.barrier()\n",
    "\n",
    "generic = initc.compose(qc)\n",
    "\n",
    "generic.draw('mpl', style={'name': 'iqx'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "76cd857e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute\n",
    "results = simulator.run(generic, shots=shots,noise_model = noise_model).result()\n",
    "answer = results.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f4a2798a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01': 10, '11': 1, '10': 17, '00': 2020}\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "\n",
    "plt.suptitle(\"Syndrome measurement with hardware noise model\")\n",
    "plt.bar(answer.keys(), answer.values(), color='royalblue')\n",
    "plt.show()\n",
    "\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7acb60c2",
   "metadata": {},
   "source": [
    "5) In general, gates are the source of errors during the time of our computation. Adding more gates means adding noise and therefore errors (remember the error rates indicated in the IBM Q Experience homepage). In particular, two-qubits gates are the gates with the higher error rate, this is the reason why we are going to implement an initialization circuits that ends with the preparation of |000> and contains some CNOTs in order to spot how the errors are more likely to appear in this case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "3a2fa9bd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/clemensgiuliani/epfl/teaching/qc24/venv_qiskit/lib/python3.12/site-packages/qiskit/visualization/circuit/matplotlib.py:269: UserWarning: Style JSON file 'iqx.json' not found in any of these locations: /Users/clemensgiuliani/epfl/teaching/qc24/venv_qiskit/lib/python3.12/site-packages/qiskit/visualization/circuit/styles/iqx.json, iqx.json. Will use default style.\n",
      "  self._style, def_font_ratio = load_style(self._style)\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2178.22x535.111 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Long initialization circuit to recreate |000>\n",
    "\n",
    "init = QuantumRegister(3,\"code\")\n",
    "initc = QuantumCircuit(init, QuantumRegister(2), ClassicalRegister(2))\n",
    "#1\n",
    "initc.x(init[0])\n",
    "initc.cx(init[0],init[1])\n",
    "initc.cx(init[1],init[2])\n",
    "\n",
    "initc.cx(init[2],init[1])\n",
    "initc.cx(init[2],init[0])\n",
    "initc.x(init[2])\n",
    "\n",
    "#2\n",
    "initc.x(init[0])\n",
    "initc.cx(init[0],init[1])\n",
    "initc.cx(init[1],init[2])\n",
    "\n",
    "initc.cx(init[2],init[1])\n",
    "initc.cx(init[2],init[0])\n",
    "initc.x(init[2])\n",
    "\n",
    "#3\n",
    "initc.x(init[0])\n",
    "initc.cx(init[0],init[1])\n",
    "initc.cx(init[1],init[2])\n",
    "\n",
    "initc.cx(init[2],init[1])\n",
    "initc.cx(init[2],init[0])\n",
    "initc.x(init[2])\n",
    "initc.barrier()\n",
    "\n",
    "# append it at the beginning of our QECC\n",
    "\n",
    "final = initc.compose(qc)\n",
    "\n",
    "final.draw('mpl', style={'name': 'iqx'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d411c60d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Execute\n",
    "results = simulator.run(final, shots=shots,noise_model = noise_model).result()\n",
    "answer = results.get_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b7409343",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'01': 40, '11': 35, '10': 44, '00': 1929}\n"
     ]
    }
   ],
   "source": [
    "# Print the results\n",
    "\n",
    "plt.suptitle(\"Syndrome meaurement for a long circuit\")\n",
    "plt.bar(answer.keys(), answer.values(), color='royalblue')\n",
    "plt.show()\n",
    "\n",
    "print(answer)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67ef480e",
   "metadata": {},
   "source": [
    "We can see how the percentage of errors increased to more than the $5\\%$ by adding gates to the circuit."
   ]
  }
 ],
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