{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Averaged Circuit Eigenvalue Sampling with Cirq Superstaq"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/SupertechLabs/client-superstaq/blob/main/docs/source/apps/aces/aces_css.ipynb) [![Launch Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/SupertechLabs/client-superstaq/HEAD?labpath=docs/source/apps/aces/aces_css.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook demonstrates how to characterize a quantum device using the averaged circuit eigenvalue sampling (ACES) protocol through Superstaq. This protocol is integrated into Superstaq following the [*original paper*](https://arxiv.org/abs/2108.05803) by Steven T. Flammia and can be accessed using `cirq-superstaq`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports and API Token"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This example tutorial notebook uses `cirq-superstaq`, our Superstaq client for Cirq,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "try:\n",
    "    import cirq_superstaq as css\n",
    "except ImportError:\n",
    "    print(\"Installing cirq-superstaq...\")\n",
    "    %pip install --quiet 'cirq-superstaq[examples]'\n",
    "    print(\"Installed cirq-superstaq.\")\n",
    "    print(\"You may need to restart the kernel to import newly installed packages.\")\n",
    "    import cirq_superstaq as css"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Required imports\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To interface Superstaq via Cirq, we must first instantiate a service provider in `cirq-superstaq` with `Service()`. We then supply a Superstaq API token (or key) by either providing the API token as an argument of `css.Service()` or by setting it as an environment variable (see more details [here](https://superstaq.readthedocs.io/en/latest/get_started/basics/basics_css.html#Set-up-access-to-Superstaq%E2%80%99s-API))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get cirq-superstaq service for Superstaq backend\n",
    "service = css.Service()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACES"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To submit an ACES job, you will need the following information:\n",
    "\n",
    "* `target`: device that you want to characterize.\n",
    "* `qubits`: indices of the qubits to characterize.\n",
    "* `shots`: number of shots to use per circuit to run.\n",
    "* `num_circuits`: number of random circuits to sample in order to get the gate eigenvalues.\n",
    "* `mirror_depth`: for each circuit, how many mirrored moments to include.\n",
    "* `extra_depth`: for each circuit, how many random moments to include.\n",
    "* `method`: the type of execution method. If `method=\"noise-sim\"`, then optional arguments `noise` and `error_prob` must be passed as well.\n",
    "\n",
    "With this information, you can submit an ACES job through the `submit_aces` method. This will take care of constructing the circuits needed to perform the protocol and will execute them. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "job_id = service.submit_aces(\n",
    "    target=\"ss_unconstrained_simulator\",\n",
    "    qubits=[0, 1],\n",
    "    shots=100,\n",
    "    num_circuits=5,\n",
    "    mirror_depth=4,\n",
    "    extra_depth=7,\n",
    "    method=\"dry-run\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c4428c69-5048-43f9-99e2-f005620d4134'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "job_id"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We note that the circuits created by this protocol can take long to execute, especially if submitted to a real device, so it might be convenient to save the job id somewhere. Once the jobs have finished running, calling `process_aces` and passing it the job id will compute the individual gate eigenvalues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "result = service.process_aces(job_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.,\n",
       "       1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A gate eigenvalue is a measure of how well a gate is executed by the device we are characterizing. It is given by the following equation\n",
    "$$ \\tilde{G}(P) = \\Lambda_{G, P} G(P) $$\n",
    "where $\\tilde{G}$ is the noisy implementation of the ideal gate $G$, $P$ is a Pauli operator, and $\\Lambda_{G, P}$ is the gate eigenvalue corresponding to that gate and Pauli pair. $G(P)$ is the conjugation of $P$ by $G$, i.e., $G P G^\\dagger$.\n",
    "\n",
    "The output is a list of estimated circuit eigenvalues. For each qubit, we consider six Clifford gates, given by the XZ maps: XZ, ZX, -YZ, -XY, ZY, and YX. For each of these, there are three eigenvalues: X, Y, and Z. All the one-qubit eigenvalues are returned first. Then, the only two-qubit gate considered is the CZ in linear connectivity. For this gate, there are 15 eigenvalues: XX, XY, XZ, XI, YX, YY, YZ, YI, ZX, ZY, ZZ, ZI, IX, IY, and IZ. Therefore, for the above example of two qubits, there are $18\\cdot2 + 14 = 51$ eigenvalues. \n",
    "\n",
    "Since we used `\"ss_unconstrained_simulator\"` as a target, the results are not very interesting because all the circuits are simulated without a noise model, so we get that every circuit eigenvalue is 1 since every gate is simulated perfectly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ACES with noise"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A more interesting example is given when we use `method=\"noise-sim\"`. We now have to specify the arguments `noise` and `error_prob` (see the [documentation](https://superstaq.readthedocs.io/en/latest/cirq_superstaq.html#cirq_superstaq.service.Service.submit_aces) for more information on these). Here, we are going to use an asymmetric depolarizing channel, with the error probabilities 0.05, 0.11, and 0.08 for the X, Y, and Z gates respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_job_id = service.submit_aces(\n",
    "    target=\"ss_unconstrained_simulator\",\n",
    "    qubits=[0, 1],\n",
    "    shots=100,\n",
    "    num_circuits=5,\n",
    "    mirror_depth=4,\n",
    "    extra_depth=7,\n",
    "    method=\"noise-sim\",\n",
    "    noise=\"asymmetric_depolarize\",\n",
    "    error_prob=(0.05, 0.11, 0.08),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d0dfbdd2-6945-4930-bbea-f6538f517004'"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "noise_job_id"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "noise_result = service.process_aces(noise_job_id)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.        , 1.        , 0.43729089, 1.        , 1.        ,\n",
       "       1.        , 0.37156509, 1.        , 0.62674301, 0.85195808,\n",
       "       1.        , 0.93597888, 0.91775969, 1.        , 1.        ,\n",
       "       1.        , 0.33541873, 1.        , 1.        , 1.        ,\n",
       "       0.95034448, 1.        , 1.        , 1.        , 1.        ,\n",
       "       0.40710778, 1.        , 1.        , 1.        , 0.87884797,\n",
       "       1.        , 0.84014817, 0.34949293, 1.        , 0.66028861,\n",
       "       1.        , 1.        , 1.        , 0.49340862, 1.        ,\n",
       "       1.        , 0.21858458, 1.        , 0.25613083, 1.        ,\n",
       "       1.        , 1.        , 0.4963732 , 1.        , 1.        ,\n",
       "       0.89530943])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(noise_result)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output is now much more interesting, since we have a more diverse set of eigenvalues due to the noise channel we have introduced."
   ]
  }
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