ReverseEngineerAlmadenParameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### I'm using this notebook to figure out what the missing parameters in the FakeAlmaden hamiltonian are. So far, I know that omegad0 is 0.1675, omegad1 is 0.176, jq0q1 is -0.0069.\n",
    "----"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Setting up pulse simulator:\n",
    "\n",
    "1) switch to openpulse-sim branch of qiskit-aer, also include this pull request https://github.com/Qiskit/qiskit-aer/pull/451 and increase `nsteps` in qiskit-aer/qiskit/providers/aer/openpulse/solver/options.py (I upped 100x to 50000)\n",
    "\n",
    "2) run `python setup.py build_ext --inplace`\n",
    "\n",
    "3) `python ./setup.py bdist_wheel`\n",
    "\n",
    "4) `cd dist` and `pip install` the wheel\n",
    "\n",
    "5) `rm -rf /Users/pranavgokhale/anaconda3/envs/QiskitDevenv/lib/python3.7/site-packages/qiskit` (replace with your username / site-packages location)\n",
    "\n",
    "6) `ln -s /Users/pranavgokhale/Developer/qiskit/qiskit-aer/qiskit/ /Users/pranavgokhale/anaconda3/envs/QiskitDevenv/lib/python3.7/site-packages/qiskit` (tailor paths to your setup)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import general libraries (needed for functions)\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.optimize import curve_fit\n",
    "\n",
    "#Import Qiskit classes classes\n",
    "import qiskit\n",
    "#from qiskit.providers.aer.noise import NoiseModel\n",
    "#from qiskit.providers.aer.noise.errors.standard_errors import depolarizing_error, thermal_relaxation_error\n",
    "\n",
    "import qiskit.pulse as pulse\n",
    "import qiskit.pulse.pulse_lib as pulse_lib\n",
    "from qiskit.compiler import assemble\n",
    "import random\n",
    "from qiskit.test.mock import FakeAlmaden"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/pranavgokhale/Developer/qiskit/qiskit-terra/qiskit/pulse/channels/pulse_channel_spec.py:87: DeprecationWarning: The PulseChannelSpec is deprecated. Use backend.configuration() instead. The supported methods require some migrations; check out the release notes for the complete details.\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "#Get a pulse configuration from the fake backend\n",
    "backend_real = FakeAlmaden()\n",
    "back_config = backend_real.configuration().to_dict()\n",
    "system = pulse.PulseChannelSpec.from_backend(backend_real)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get pulse simulator backend\n",
    "backend_sim = qiskit.Aer.get_backend('pulse_simulator')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "cmd_def = qiskit.pulse.CmdDef.from_defaults(backend_real.defaults().cmd_def, backend_real.defaults().pulse_library)\n",
    "measure = cmd_def.get('measure', qubits=backend_real.configuration().meas_map[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule = cmd_def.get('x', qubits=[0])\n",
    "schedule |= measure << schedule.duration\n",
    "schedules = [schedule]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Design the Hamiltonian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's build a transmon Hamiltonian with anharmonicity to test the Rabi oscillation and CR"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "hamiltonian = {}\n",
    "hamiltonian['h_str'] = ['_SUM[i,0,1,wq{i}/2*(I{i}-Z{i})]', '_SUM[i,0,1,omegad{i}*X{i}||D{i}]',\n",
    "                        'jq0q1*Sp0*Sm1', 'jq0q1*Sm0*Sp1', 'omegad1*X0||U0', 'omegad0*X1||U1']\n",
    "hamiltonian['qub'] = {'0': 2, '1': 2}\n",
    "hamiltonian['vars'] = {'wq0': 30.518812656662774, 'wq1': 31.238229295532093,\n",
    "                       'omegad0': 0.1675, 'omegad1': 0.176,\n",
    "                       'jq0q1': -0.0069}\n",
    "\n",
    "back_config['hamiltonian'] = hamiltonian\n",
    "back_config['noise'] = {}\n",
    "back_config['dt'] = 1.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "back_config['ode_options'] = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "back_config['qubit_list'] = [0,1]\n",
    "#back_config['qubit_list'] = list(range(20))\n",
    "#back_config['qubit_list'] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots = 10000\n",
    "rabi_qobj = assemble(schedules, backend_real, \n",
    "                     meas_level=1, meas_return='avg', \n",
    "                     memory_slots=2,\n",
    "                     shots=shots, sim_config = back_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "evals, estates = backend_sim.get_dressed_energies(rabi_qobj)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 4.85720941, 4.97172909, 9.82893851])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "evals/2/np.pi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "rabi_qobj = assemble(schedules, backend_real, \n",
    "                     qubit_lo_freq = [evals[1]/2/np.pi, evals[2]/2/np.pi],\n",
    "                     shots=shots, sim_config = back_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### omegad0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_result = backend_sim.run(rabi_qobj).result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = []\n",
    "omegad0s = np.linspace(0.166, 0.169, 10)\n",
    "for omegad0 in omegad0s:\n",
    "    back_config['hamiltonian']['vars']['omegad0'] = omegad0\n",
    "    rabi_qobj = assemble(schedules, backend_real, \n",
    "                         qubit_lo_freq = [evals[1]/2/np.pi, evals[2]/2/np.pi],\n",
    "                         shots=20000, sim_config = back_config)\n",
    "    sim_result = backend_sim.run(rabi_qobj).result()\n",
    "    counts.append(sim_result.get_counts().get('00000000000000000001', 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12e4d0c88>]"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(omegad0s, counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "back_config['hamiltonian']['vars']['omegad0'] = 0.1675"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----\n",
    "## omegad1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule = cmd_def.get('x', qubits=[1])\n",
    "schedule |= measure << schedule.duration\n",
    "schedules = [schedule]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = []\n",
    "omegad1s = np.linspace(0.175, 0.179, 10)\n",
    "for omegad1 in omegad1s:\n",
    "    back_config['hamiltonian']['vars']['omegad1'] = omegad1\n",
    "    rabi_qobj = assemble(schedules, backend_real, \n",
    "                         qubit_lo_freq = [evals[1]/2/np.pi, evals[2]/2/np.pi],\n",
    "                         shots=10000, sim_config = back_config)\n",
    "    sim_result = backend_sim.run(rabi_qobj).result()\n",
    "    counts.append(sim_result.get_counts().get('00000000000000000010', 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12aad8198>]"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(omegad1s, counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "back_config['hamiltonian']['vars']['omegad1'] = 0.176"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----\n",
    "## jq0q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pulse_compiler_helper_fns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "schedule = cmd_def.get('x', qubits=[0])\n",
    "schedule |= cmd_def.get('cx', qubits=[0, 1]) << schedule.duration\n",
    "schedule = schedule.filter(channels=[system.qubits[0].drive, system.qubits[1].drive, system.controls[0]])\n",
    "schedule |= measure << schedule.duration\n",
    "qobj = assemble(schedule, backend_real, \n",
    "                qubit_lo_freq = [evals[1]/2/np.pi, evals[2]/2/np.pi],\n",
    "                shots=10000, sim_config = back_config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "counts = []\n",
    "jq0q1s = np.linspace(-0.0069-0.001, -0.0069+0.001, 5)\n",
    "for jq0q1 in jq0q1s:\n",
    "    back_config['hamiltonian']['vars']['jq0q1'] = jq0q1\n",
    "    rabi_qobj = assemble(schedule, backend_real, \n",
    "                         qubit_lo_freq = [evals[1]/2/np.pi, evals[2]/2/np.pi],\n",
    "                         shots=10000, sim_config = back_config)\n",
    "    sim_result = backend_sim.run(rabi_qobj).result()\n",
    "    counts.append(sim_result.get_counts().get('00000000000000000011', 0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x128367b70>]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(jq0q1s, counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "back_config['hamiltonian']['vars']['jq0q1'] = -0.0069"
   ]
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}