qiskit/advanced/ignis/4_measurement_error_mitigation.ipynb

{
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![qiskit_header.png](attachment:qiskit_header.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Measurement Error Mitigation\n",
    "\n",
    "* **Last Updated:** May 2, 2019\n",
    "* **Requires:** qiskit-terra 0.8, qiskit-ignis 0.1.1, qiskit-aer 0.2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The measurement calibration is used to mitigate measurement errors. \n",
    "The main idea is to prepare all $2^n$ basis input states and compute the probability of measuring counts in the other basis states. \n",
    "From these calibrations, it is possible to correct the average results of another experiment of interest. This notebook gives examples for how to use the ``ignis.mitigation.measurement`` module."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.438505Z",
     "start_time": "2019-12-10T21:48:43.641392Z"
    }
   },
   "outputs": [],
   "source": [
    "# Import general libraries (needed for functions)\n",
    "import numpy as np\n",
    "import time\n",
    "\n",
    "# Import Qiskit classes\n",
    "import qiskit \n",
    "from qiskit import QuantumRegister, QuantumCircuit, ClassicalRegister, Aer\n",
    "from qiskit.providers.aer import noise\n",
    "from qiskit.tools.visualization import plot_histogram\n",
    "\n",
    "# Import measurement calibration functions\n",
    "from qiskit.ignis.mitigation.measurement import (complete_meas_cal, tensored_meas_cal,\n",
    "                                                 CompleteMeasFitter, TensoredMeasFitter)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 Qubit Example of the Calibration Matrices"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that we would like to generate a calibration matrix for the 3 qubits Q2, Q3 and Q4 in a 5-qubit Quantum Register [Q0,Q1,Q2,Q3,Q4]. \n",
    "\n",
    "Since we have 3 qubits, there are $2^3=8$ possible quantum states."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Measurement Calibration Circuits\n",
    "\n",
    "First, we generate a list of measurement calibration circuits for the full Hilbert space. \n",
    "Each circuit creates a basis state. \n",
    "If there are $n=3$ qubits, then we get $2^3=8$ calibration circuits."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following function **complete_meas_cal** returns a list **meas_calibs** of `QuantumCircuit` objects containing the calibration circuits, \n",
    "and a list **state_labels** of the calibration state labels.\n",
    "\n",
    "The input to this function can be given in one of the following three forms:\n",
    "\n",
    "- **qubit_list:** A list of qubits to perform the measurement correction on, or:\n",
    "- **qr (QuantumRegister):** A quantum register, or:\n",
    "- **cr (ClassicalRegister):** A classical register.\n",
    "\n",
    "In addition, one can provide a string **circlabel**, which is added at the beginning of the circuit names for unique identification.\n",
    "\n",
    "For example, in our case, the input is a 5-qubit `QuantumRegister` containing the qubits Q2,Q3,Q4:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.445285Z",
     "start_time": "2019-12-10T21:48:45.440921Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generate the calibration circuits\n",
    "qr = qiskit.QuantumRegister(5)\n",
    "qubit_list = [2,3,4]\n",
    "meas_calibs, state_labels = complete_meas_cal(qubit_list=qubit_list, qr=qr, circlabel='mcal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Print the $2^3=8$ state labels (for the 3 qubits Q2,Q3,Q4):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.454762Z",
     "start_time": "2019-12-10T21:48:45.448344Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['000', '001', '010', '011', '100', '101', '110', '111']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_labels"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Computing the Calibration Matrix\n",
    "\n",
    "If we do not apply any noise, then the calibration matrix is expected to be the $8 \\times 8$ identity matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.564448Z",
     "start_time": "2019-12-10T21:48:45.456937Z"
    }
   },
   "outputs": [],
   "source": [
    "# Execute the calibration circuits without noise\n",
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "job = qiskit.execute(meas_calibs, backend=backend, shots=1000)\n",
    "cal_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.572068Z",
     "start_time": "2019-12-10T21:48:45.566831Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1. 0. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 1. 0. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 1. 0. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 1. 0. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 1. 0. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 1. 0. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 1. 0.]\n",
      " [0. 0. 0. 0. 0. 0. 0. 1.]]\n"
     ]
    }
   ],
   "source": [
    "# The calibration matrix without noise is the identity matrix\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, circlabel='mcal')\n",
    "print(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Assume that we apply some noise model from Qiskit Aer to the 5 qubits, \n",
    "then the calibration matrix will have most of its mass on the main diagonal, with some additional 'noise'.\n",
    "\n",
    "Alternatively, we can execute the calibration circuits using an IBMQ provider."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.578362Z",
     "start_time": "2019-12-10T21:48:45.574278Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generate a noise model for the 5 qubits\n",
    "noise_model = noise.NoiseModel()\n",
    "for qi in range(5):\n",
    "    read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],[0.25,0.75]])\n",
    "    noise_model.add_readout_error(read_err, [qi])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.703495Z",
     "start_time": "2019-12-10T21:48:45.580438Z"
    }
   },
   "outputs": [],
   "source": [
    "# Execute the calibration circuits\n",
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "job = qiskit.execute(meas_calibs, backend=backend, shots=1000, noise_model=noise_model)\n",
    "cal_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.715380Z",
     "start_time": "2019-12-10T21:48:45.709472Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.715 0.207 0.2   0.05  0.198 0.044 0.064 0.015]\n",
      " [0.093 0.608 0.02  0.166 0.024 0.168 0.003 0.043]\n",
      " [0.072 0.017 0.595 0.155 0.019 0.004 0.165 0.051]\n",
      " [0.012 0.06  0.082 0.534 0.003 0.023 0.013 0.133]\n",
      " [0.089 0.024 0.019 0.005 0.612 0.169 0.155 0.06 ]\n",
      " [0.01  0.076 0.003 0.017 0.08  0.522 0.019 0.13 ]\n",
      " [0.009 0.003 0.076 0.017 0.058 0.016 0.526 0.109]\n",
      " [0.    0.005 0.005 0.056 0.006 0.054 0.055 0.459]]\n"
     ]
    }
   ],
   "source": [
    "# Calculate the calibration matrix with the noise model\n",
    "meas_fitter = CompleteMeasFitter(cal_results, state_labels, qubit_list=qubit_list, circlabel='mcal')\n",
    "print(meas_fitter.cal_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.911350Z",
     "start_time": "2019-12-10T21:48:45.718844Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the calibration matrix\n",
    "meas_fitter.plot_calibration()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing the Results\n",
    "\n",
    "We would like to compute the total measurement fidelity, and the measurement fidelity for a specific qubit, for example, Q0.\n",
    "\n",
    "Since the on-diagonal elements of the calibration matrix are the probabilities of measuring state 'x' given preparation of state 'x', \n",
    "then the trace of this matrix is the average assignment fidelity.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.918657Z",
     "start_time": "2019-12-10T21:48:45.913417Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average Measurement Fidelity: 0.571375\n",
      "Average Measurement Fidelity of Q0: 0.827000\n"
     ]
    }
   ],
   "source": [
    "# What is the measurement fidelity?\n",
    "print(\"Average Measurement Fidelity: %f\" % meas_fitter.readout_fidelity())\n",
    "\n",
    "# What is the measurement fidelity of Q0?\n",
    "print(\"Average Measurement Fidelity of Q0: %f\" % meas_fitter.readout_fidelity(\n",
    "    label_list = [['000','001','010','011'],['100','101','110','111']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applying the Calibration\n",
    "\n",
    "We now perform another experiment and correct the measured results. \n",
    "\n",
    "## Correct Measurement Noise on a 3Q GHZ State\n",
    "\n",
    "As an example, we start with the 3-qubit GHZ state on the qubits Q2,Q3,Q4:\n",
    "\n",
    "$$ \\mid GHZ \\rangle = \\frac{\\mid{000} \\rangle + \\mid{111} \\rangle}{\\sqrt{2}}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:45.928489Z",
     "start_time": "2019-12-10T21:48:45.920726Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1a26c80bd0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a 3Q GHZ state\n",
    "cr = ClassicalRegister(3)\n",
    "ghz = QuantumCircuit(qr, cr)\n",
    "ghz.h(qr[2])\n",
    "ghz.cx(qr[2], qr[3])\n",
    "ghz.cx(qr[3], qr[4])\n",
    "ghz.measure(qr[2],cr[0])\n",
    "ghz.measure(qr[3],cr[1])\n",
    "ghz.measure(qr[4],cr[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now execute the calibration circuits (with the noise model above):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.069988Z",
     "start_time": "2019-12-10T21:48:45.930197Z"
    }
   },
   "outputs": [],
   "source": [
    "job = qiskit.execute([ghz], backend=backend, shots=5000, noise_model=noise_model)\n",
    "results = job.result()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now compute the results without any error mitigation and with the mitigation, namely after applying the calibration matrix to the results.\n",
    "\n",
    "There are two fitting methods for applying the calibration (if no method is defined, then 'least_squares' is used). \n",
    "- **'pseudo_inverse'**, which is a direct inversion of the calibration matrix, \n",
    "- **'least_squares'**, which constrains to have physical probabilities.\n",
    "\n",
    "The raw data to be corrected can be given in a number of forms:\n",
    "\n",
    "- Form1: A counts dictionary from results.get_counts,\n",
    "- Form2: A list of counts of length=len(state_labels),\n",
    "- Form3: A list of counts of length=M*len(state_labels) where M is an integer (e.g. for use with the tomography data),\n",
    "- Form4: A qiskit Result (e.g. results as above)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.085288Z",
     "start_time": "2019-12-10T21:48:46.072367Z"
    }
   },
   "outputs": [],
   "source": [
    "# Results without mitigation\n",
    "raw_counts = results.get_counts()\n",
    "\n",
    "# Get the filter object\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now plot the results with and without error mitigation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.320336Z",
     "start_time": "2019-12-10T21:48:46.087649Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from qiskit.tools.visualization import *\n",
    "plot_histogram([raw_counts, mitigated_counts], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Applying to a reduced subset of qubits"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Consider now that we want to correct a 2Q Bell state, but we have the 3Q calibration matrix. We can reduce the matrix and build a new mitigation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.329509Z",
     "start_time": "2019-12-10T21:48:46.322471Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1a26e56d10>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a 2Q Bell state between Q2 and Q4\n",
    "cr = ClassicalRegister(2)\n",
    "bell = QuantumCircuit(qr, cr)\n",
    "bell.h(qr[2])\n",
    "bell.cx(qr[2], qr[4])\n",
    "bell.measure(qr[2],cr[0])\n",
    "bell.measure(qr[4],cr[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.363702Z",
     "start_time": "2019-12-10T21:48:46.331739Z"
    }
   },
   "outputs": [],
   "source": [
    "job = qiskit.execute([bell], backend=backend, shots=5000, noise_model=noise_model)\n",
    "results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.368672Z",
     "start_time": "2019-12-10T21:48:46.365428Z"
    }
   },
   "outputs": [],
   "source": [
    "#build a fitter from the subset\n",
    "meas_fitter_sub = meas_fitter.subset_fitter(qubit_sublist=[2,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.375831Z",
     "start_time": "2019-12-10T21:48:46.371066Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.791 , 0.2145, 0.223 , 0.057 ],\n",
       "       [0.1035, 0.684 , 0.0215, 0.1835],\n",
       "       [0.0965, 0.0245, 0.6755, 0.177 ],\n",
       "       [0.009 , 0.077 , 0.08  , 0.5825]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#The calibration matrix is now in the space Q2/Q4\n",
    "meas_fitter_sub.cal_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.529639Z",
     "start_time": "2019-12-10T21:48:46.378317Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Results without mitigation\n",
    "raw_counts = results.get_counts()\n",
    "\n",
    "# Get the filter object\n",
    "meas_filter_sub = meas_fitter_sub.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter_sub.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)\n",
    "from qiskit.tools.visualization import *\n",
    "plot_histogram([raw_counts, mitigated_counts], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tensored mitigation\n",
    "\n",
    "The calibration can be simplified if the error is known to be local. By \"local error\" we mean that the error can be tensored to subsets of qubits. In this case, less than $2^n$ states are needed for the computation of the calibration matrix.\n",
    "\n",
    "Assume that the error acts locally on qubit 2 and the pair of qubits 3 and 4. Construct the calibration circuits by using the function `tensored_meas_cal`. Unlike before we need to explicitly divide the qubit list up into subset regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.537248Z",
     "start_time": "2019-12-10T21:48:46.532112Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generate the calibration circuits\n",
    "qr = qiskit.QuantumRegister(5)\n",
    "mit_pattern = [[2],[3,4]]\n",
    "meas_calibs, state_labels = tensored_meas_cal(mit_pattern=mit_pattern, qr=qr, circlabel='mcal')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now retrieve the names of the generated circuits. Note that in each label (of length 3), the least significant bit corresponds to qubit 2, the middle bit corresponds to qubit 3, and the most significant bit corresponds to qubit 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.546122Z",
     "start_time": "2019-12-10T21:48:46.539698Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mcalcal_000\n",
      "mcalcal_010\n",
      "mcalcal_101\n",
      "mcalcal_111\n"
     ]
    }
   ],
   "source": [
    "for circ in meas_calibs:\n",
    "    print(circ.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us elaborate on the circuit names. We see that there are only four circuits, instead of eight. The total number of required circuits is $2^m$ where $m$ is the number of qubits in the larget subset (here $m=2$).\n",
    "\n",
    "Each basis state of qubits 3 and 4 appears exactly once. Only two basis states are required for qubit 2, so these are split equally across the four experiments. For example, state '0' of qubit 2 appears in state labels '000' and '010'.\n",
    "\n",
    "We now execute the calibration circuits on an Aer simulator, using the same noise model as before. This noise is in fact local to qubits 3 and 4 separately, but assume that we don't know it, and that we only know that it is local for qubit 2."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.553810Z",
     "start_time": "2019-12-10T21:48:46.548922Z"
    }
   },
   "outputs": [],
   "source": [
    "# Generate a noise model for the 5 qubits\n",
    "noise_model = noise.NoiseModel()\n",
    "for qi in range(5):\n",
    "    read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],[0.25,0.75]])\n",
    "    noise_model.add_readout_error(read_err, [qi])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.731689Z",
     "start_time": "2019-12-10T21:48:46.560852Z"
    }
   },
   "outputs": [],
   "source": [
    "# Execute the calibration circuits\n",
    "backend = qiskit.Aer.get_backend('qasm_simulator')\n",
    "job = qiskit.execute(meas_calibs, backend=backend, shots=5000, noise_model=noise_model)\n",
    "cal_results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.742193Z",
     "start_time": "2019-12-10T21:48:46.737348Z"
    }
   },
   "outputs": [],
   "source": [
    "meas_fitter = TensoredMeasFitter(cal_results, mit_pattern=mit_pattern)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The fitter provides two calibration matrices. One matrix is for qubit 2, and the other matrix is for qubits 3 and 4."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.749341Z",
     "start_time": "2019-12-10T21:48:46.744734Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[array([[0.8982, 0.2482],\n",
      "       [0.1018, 0.7518]]), array([[0.8024, 0.219 , 0.2328, 0.057 ],\n",
      "       [0.0844, 0.6816, 0.0268, 0.1826],\n",
      "       [0.103 , 0.0226, 0.674 , 0.1908],\n",
      "       [0.0102, 0.0768, 0.0664, 0.5696]])]\n"
     ]
    }
   ],
   "source": [
    "print(meas_fitter.cal_matrices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can look at the readout fidelities of the individual tensored components or qubits within a set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:46.758432Z",
     "start_time": "2019-12-10T21:48:46.752543Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Readout fidelity of Q2: 0.825000\n",
      "Readout fidelity of Q3/4 space (e.g. mean assignment \n",
      "fidelity of 00,10,01 and 11): 0.681900\n",
      "Readout fidelity of Q3: 0.830700\n"
     ]
    }
   ],
   "source": [
    "#readout fidelity of Q2\n",
    "print('Readout fidelity of Q2: %f'%meas_fitter.readout_fidelity(0))\n",
    "\n",
    "#readout fidelity of Q3/Q4\n",
    "print('Readout fidelity of Q3/4 space (e.g. mean assignment '\n",
    "      '\\nfidelity of 00,10,01 and 11): %f'%meas_fitter.readout_fidelity(1))\n",
    "\n",
    "#readout fidelity of Q3\n",
    "print('Readout fidelity of Q3: %f'%meas_fitter.readout_fidelity(1,[['00','10'],['01','11']]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the individual calibration matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:47.035847Z",
     "start_time": "2019-12-10T21:48:46.760848Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q2 Calibration Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Q3/Q4 Calibration Matrix\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the calibration matrix\n",
    "print('Q2 Calibration Matrix')\n",
    "meas_fitter.plot_calibration(0)\n",
    "print('Q3/Q4 Calibration Matrix')\n",
    "meas_fitter.plot_calibration(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:47.043630Z",
     "start_time": "2019-12-10T21:48:47.037645Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<qiskit.circuit.instructionset.InstructionSet at 0x1a26c80a50>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Make a 3Q GHZ state\n",
    "cr = ClassicalRegister(3)\n",
    "ghz = QuantumCircuit(qr, cr)\n",
    "ghz.h(qr[2])\n",
    "ghz.cx(qr[2], qr[3])\n",
    "ghz.cx(qr[3], qr[4])\n",
    "ghz.measure(qr[2],cr[0])\n",
    "ghz.measure(qr[3],cr[1])\n",
    "ghz.measure(qr[4],cr[2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We now execute the calibration circuits (with the noise model above):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:47.084467Z",
     "start_time": "2019-12-10T21:48:47.045697Z"
    }
   },
   "outputs": [],
   "source": [
    "job = qiskit.execute([ghz], backend=backend, shots=5000, noise_model=noise_model)\n",
    "results = job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:47.120453Z",
     "start_time": "2019-12-10T21:48:47.086219Z"
    }
   },
   "outputs": [],
   "source": [
    "# Results without mitigation\n",
    "raw_counts = results.get_counts()\n",
    "\n",
    "# Get the filter object\n",
    "meas_filter = meas_fitter.filter\n",
    "\n",
    "# Results with mitigation\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot the raw vs corrected state:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:47.374548Z",
     "start_time": "2019-12-10T21:48:47.122221Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_filter = meas_fitter.filter\n",
    "mitigated_results = meas_filter.apply(results)\n",
    "mitigated_counts = mitigated_results.get_counts(0)\n",
    "plot_histogram([raw_counts, mitigated_counts], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As a check we should get the same answer if we build the full correction matrix from a tensor product of the subspace calibration matrices:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:47.655680Z",
     "start_time": "2019-12-10T21:48:47.377175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meas_calibs2, state_labels2 = complete_meas_cal([2,3,4])\n",
    "meas_fitter2 = CompleteMeasFitter(None, state_labels2)\n",
    "meas_fitter2.cal_matrix = np.kron(meas_fitter.cal_matrices[1],meas_fitter.cal_matrices[0])\n",
    "meas_filter2 = meas_fitter2.filter\n",
    "mitigated_results2 = meas_filter2.apply(results)\n",
    "mitigated_counts2 = mitigated_results2.get_counts(0)\n",
    "plot_histogram([raw_counts, mitigated_counts2], legend=['raw', 'mitigated'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2019-12-10T21:48:47.743047Z",
     "start_time": "2019-12-10T21:48:47.658283Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<h3>Version Information</h3><table><tr><th>Qiskit Software</th><th>Version</th></tr><tr><td>Qiskit</td><td>0.14.0</td></tr><tr><td>Terra</td><td>0.11.0</td></tr><tr><td>Aer</td><td>0.3.4</td></tr><tr><td>Ignis</td><td>0.2.0</td></tr><tr><td>Aqua</td><td>0.6.1</td></tr><tr><td>IBM Q Provider</td><td>0.4.4</td></tr><tr><th>System information</th></tr><tr><td>Python</td><td>3.7.5 (default, Oct 25 2019, 10:52:18) \n",
       "[Clang 4.0.1 (tags/RELEASE_401/final)]</td></tr><tr><td>OS</td><td>Darwin</td></tr><tr><td>CPUs</td><td>4</td></tr><tr><td>Memory (Gb)</td><td>16.0</td></tr><tr><td colspan='2'>Tue Dec 10 16:48:47 2019 EST</td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div style='width: 100%; background-color:#d5d9e0;padding-left: 10px; padding-bottom: 10px; padding-right: 10px; padding-top: 5px'><h3>This code is a part of Qiskit</h3><p>&copy; Copyright IBM 2017, 2019.</p><p>This code is licensed under the Apache License, Version 2.0. You may<br>obtain a copy of this license in the LICENSE.txt file in the root directory<br> of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.<p>Any modifications or derivative works of this code must retain this<br>copyright notice, and modified files need to carry a notice indicating<br>that they have been altered from the originals.</p></div>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import qiskit.tools.jupyter\n",
    "%qiskit_version_table\n",
    "%qiskit_copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.7.5"
  },
  "varInspector": {
   "cols": {
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    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
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   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}