{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Robust Phase Estimation (RPE) Tutorial\n",
    "This notebook demonstrates how to use Robust Phase Estimation (RPE) to estimate certain parameters of a standard single-qubit model.  The RPE protocol is contained within the `extras` package of pyGSTi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import relevant namespaces.\n",
    "\n",
    "import pygsti\n",
    "from pygsti.modelpacks.legacy import std1Q_XY as Std1Q_XY\n",
    "from pygsti.extras import rpe\n",
    "\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Declare the particular RPE instance we are interested in\n",
    "#(X and Y pi/2 rotations)\n",
    "#(Prep and measurement are for the |0> state.   See below for prep and measure in |0> and |1>, respectively.)\n",
    "rpeconfig_inst = rpe.rpeconfig_GxPi2_GyPi2_00"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Declare a variety of relevant parameters\n",
    "\n",
    "target_model = Std1Q_XY.target_model()\n",
    "target_model.set_all_parameterizations('TP')\n",
    "maxLengths_1024 = [1,2,4,8,16,32,64,128,256,512,1024]\n",
    "\n",
    "stringListsRPE = rpe.rpeconstruction.create_rpe_angle_circuits_dict(10,rpeconfig_inst)\n",
    "\n",
    "angleList = ['alpha','epsilon','theta']\n",
    "\n",
    "numStrsD = {}\n",
    "numStrsD['RPE'] = [6*i for i in np.arange(1,12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create noisy model\n",
    "mdl_real = target_model.randomize_with_unitary(.01,seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract noisy model angles\n",
    "true_alpha = rpe.extract_alpha(mdl_real,rpeconfig_inst)\n",
    "true_epsilon = rpe.extract_epsilon(mdl_real,rpeconfig_inst)\n",
    "true_theta = rpe.extract_theta(mdl_real,rpeconfig_inst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Simulate dataset\n",
    "N=1000\n",
    "DS = pygsti.construction.simulate_data(mdl_real,stringListsRPE['totalStrList'],N,sample_error='binomial',seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyze dataset\n",
    "resultsRPE = rpe.analyze_rpe_data(DS,mdl_real,stringListsRPE,rpeconfig_inst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Print results\n",
    "print('alpha_true - pi/2 =',true_alpha-np.pi/2)\n",
    "print('epsilon_true - pi/2 =',true_epsilon-np.pi/2)\n",
    "print('theta_true =',true_theta)\n",
    "print()\n",
    "print('alpha_true - alpha_est_final =',resultsRPE['alphaErrorList'][-1])\n",
    "print('epsilon_true - epsilon_est_final =',resultsRPE['epsilonErrorList'][-1])\n",
    "print('theta_true - theta_est_final =',resultsRPE['thetaErrorList'][-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Repeat above with prep and measure in |0> and |1>, respectively.)\n",
    "rpeconfig_inst = rpe.rpeconfig_GxPi2_GyPi2_UpDn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Declare a variety of relevant parameters\n",
    "target_model = pygsti.construction.create_explicit_model([('Q0',)], ['Gx','Gy'],[ \"X(pi/2,Q0)\", \"Y(pi/2,Q0)\"],\n",
    "                                  effect_expressions=['1','0'])\n",
    "target_model.set_all_parameterizations('TP')\n",
    "maxLengths_1024 = [1,2,4,8,16,32,64,128,256,512,1024]\n",
    "\n",
    "stringListsRPE = rpe.rpeconstruction.create_rpe_angle_circuits_dict(10,rpeconfig_inst)\n",
    "\n",
    "angleList = ['alpha','epsilon','theta']\n",
    "\n",
    "numStrsD = {}\n",
    "numStrsD['RPE'] = [6*i for i in np.arange(1,12)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Create noisy model\n",
    "mdl_real = target_model.randomize_with_unitary(.01,seed=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Extract noisy model angles\n",
    "true_alpha = rpe.extract_alpha(mdl_real,rpeconfig_inst)\n",
    "true_epsilon = rpe.extract_epsilon(mdl_real,rpeconfig_inst)\n",
    "true_theta = rpe.extract_theta(mdl_real,rpeconfig_inst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Simulate dataset\n",
    "N=1000\n",
    "DS = pygsti.construction.simulate_data(mdl_real,stringListsRPE['totalStrList'],N,sample_error='binomial',seed=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyze dataset\n",
    "resultsRPE = rpe.analyze_rpe_data(DS,mdl_real,stringListsRPE,rpeconfig_inst)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Print results\n",
    "print('alpha_true - pi/2 =',true_alpha-np.pi/2)\n",
    "print('epsilon_true - pi/2 =',true_epsilon-np.pi/2)\n",
    "print('theta_true =',true_theta)\n",
    "print()\n",
    "print('alpha_true - alpha_est_final =',resultsRPE['alphaErrorList'][-1])\n",
    "print('epsilon_true - epsilon_est_final =',resultsRPE['epsilonErrorList'][-1])\n",
    "print('theta_true - theta_est_final =',resultsRPE['thetaErrorList'][-1])"
   ]
  },
  {
   "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": "3.8.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}