CrossResonance_Tomography.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "from collections import namedtuple\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import qiskit as q\n",
    "import qiskit.tools.jupyter\n",
    "from qiskit.pulse import pulse_lib as _  # only want to call pulse_lib as q.pulse.pulse_lib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pulse_compiler_helper_fns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<AccountProvider for IBMQ(hub='ibm-q', group='open', project='main')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-internal', group='reservations', project='reservations')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-dev', group='qiskit-terra', project='pulse-testing')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-dev', group='qiskit', project='terra')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-dev', group='support', project='new-device-test')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-dev', group='cloud', project='main')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-perf', group='performance', project='default-params')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-perf', group='performance', project='pulse-testing')>,\n",
       " <AccountProvider for IBMQ(hub='ibm-q-perf', group='qcaas', project='default')>,\n",
       " <AccountProvider for IBMQ(hub='perf-on-client-d', group='rocket', project='asplos')>]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.IBMQ.load_account() # Load account from disk\n",
    "q.IBMQ.providers()    # List all available providers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/alijavadi/Desktop/ajavadia-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": [
    "provider = q.IBMQ.get_provider(group='qiskit-terra', project='pulse-testing')\n",
    "backend = provider.get_backend('alt_almaden')\n",
    "system = q.pulse.PulseChannelSpec.from_backend(backend)\n",
    "cmd_def = q.pulse.CmdDef.from_defaults(backend.defaults().cmd_def, backend.defaults().pulse_library)\n",
    "measure = cmd_def.get('measure', qubits=backend.configuration().meas_map[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measure X on target\n",
    "x_schedules = []\n",
    "for theta in np.linspace(0, np.pi, 41, endpoint=True):\n",
    "    schedule = pulse_compiler_helper_fns.get_cr_schedule(theta, 0, 1, cmd_def, system)\n",
    "    schedule |= cmd_def.get('u2', qubits=[1], P0=0, P1=np.pi) << schedule.duration\n",
    "    schedule |= measure << schedule.duration\n",
    "    x_schedules.append(schedule)\n",
    "    \n",
    "# do same thing, but first flip \n",
    "for theta in np.linspace(0, np.pi, 41, endpoint=True):\n",
    "    schedule = cmd_def.get('x', qubits=[0])\n",
    "    schedule |= pulse_compiler_helper_fns.get_cr_schedule(theta, 0, 1, cmd_def, system) << schedule.duration\n",
    "    schedule |= cmd_def.get('u2', qubits=[1], P0=0, P1=np.pi) << schedule.duration\n",
    "    schedule |= measure << schedule.duration\n",
    "    x_schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measure Y on target\n",
    "y_schedules = []\n",
    "for theta in np.linspace(0, np.pi, 41, endpoint=True):\n",
    "    schedule = pulse_compiler_helper_fns.get_cr_schedule(theta, 0, 1, cmd_def, system)\n",
    "    schedule |= cmd_def.get('u1', qubits=[1], P0=-np.pi/2) << schedule.duration\n",
    "    schedule |= cmd_def.get('u2', qubits=[1], P0=0, P1=np.pi) << schedule.duration\n",
    "    schedule |= measure << schedule.duration\n",
    "    y_schedules.append(schedule)\n",
    "    \n",
    "# do same thing, but first flip \n",
    "for theta in np.linspace(0, np.pi, 41, endpoint=True):\n",
    "    schedule = cmd_def.get('x', qubits=[0])\n",
    "    schedule |= pulse_compiler_helper_fns.get_cr_schedule(theta, 0, 1, cmd_def, system) << schedule.duration\n",
    "    schedule |= cmd_def.get('u1', qubits=[1], P0=-np.pi/2) << schedule.duration\n",
    "    schedule |= cmd_def.get('u2', qubits=[1], P0=0, P1=np.pi) << schedule.duration\n",
    "    schedule |= measure << schedule.duration\n",
    "    y_schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Measure Z on target\n",
    "z_schedules = []\n",
    "for theta in np.linspace(0, np.pi, 41, endpoint=True):\n",
    "    schedule = pulse_compiler_helper_fns.get_cr_schedule(theta, 0, 1, cmd_def, system)\n",
    "    schedule |= measure << schedule.duration\n",
    "    z_schedules.append(schedule)\n",
    "    \n",
    "# do same thing, but first flip \n",
    "for theta in np.linspace(0, np.pi, 41, endpoint=True):\n",
    "    schedule = cmd_def.get('x', qubits=[0])\n",
    "    schedule |= pulse_compiler_helper_fns.get_cr_schedule(theta, 0, 1, cmd_def, system) << schedule.duration\n",
    "    schedule |= measure << schedule.duration\n",
    "    z_schedules.append(schedule)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/anaconda3/lib/python3.6/site-packages/ipywidgets/widgets/widget_layout.py:84: DeprecationWarning: Layout properties overflow_x and overflow_y have been deprecated and will be dropped in a future release. Please use the overflow shorthand property instead\n",
      "  warnings.warn(\"Layout properties overflow_x and overflow_y have been deprecated and will be dropped in a future release. Please use the overflow shorthand property instead\", DeprecationWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X job is 5dd816a28aa7e00018b8f567\n",
      "Y job is 5dd816bff5613200186ad323\n",
      "Z job is 5dd816d90bf24b0018b73638\n"
     ]
    }
   ],
   "source": [
    "shots = 1000\n",
    "x_job = q.execute(x_schedules, backend=backend, shots=shots)\n",
    "print('X job is %s' % x_job.job_id())\n",
    "\n",
    "y_job = q.execute(y_schedules, backend=backend, shots=shots)\n",
    "print('Y job is %s' % y_job.job_id())\n",
    "\n",
    "z_job = q.execute(z_schedules, backend=backend, shots=shots)\n",
    "print('Z job is %s' % z_job.job_id())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<JobStatus.VALIDATING: 'job is being validated'>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z_job.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BackendStatus(backend_name='alt_almaden', backend_version='1.2.4', operational=True, pending_jobs=3, status_msg='active')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "backend.status()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_result = x_job.result()\n",
    "y_result = y_job.result()\n",
    "z_result = z_job.result()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from qiskit.ignis.verification.tomography.data import marginal_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.0020000000000000018, -0.02400000000000002, 0.03600000000000003, 0.028000000000000025, 0.02400000000000002, 0.06800000000000006, 0.07800000000000007, 0.016000000000000014, 0.04400000000000004, 0.0, 0.1319999999999999, 0.10200000000000009, 0.08000000000000007, 0.06600000000000006, 0.040000000000000036, 0.08200000000000007, 0.040000000000000036, 0.10400000000000009, 0.07800000000000007, 0.014000000000000012, 0.050000000000000044, -0.020000000000000018, 0.008000000000000007, 0.030000000000000027, 0.08400000000000007, 0.006000000000000005, 0.06400000000000006, 0.04200000000000004, 0.07800000000000007, 0.07800000000000007, 0.06000000000000005, 0.06000000000000005, 0.028000000000000025, 0.1200000000000001, 0.01200000000000001, 0.050000000000000044, 0.03400000000000003, 0.062000000000000055, -0.0020000000000000018, -0.0020000000000000018, -0.05600000000000005]\n",
      "[0.028000000000000025, 0.05400000000000005, 0.04200000000000004, -0.020000000000000018, 0.0, 0.05600000000000005, 0.0, 0.08600000000000008, 0.08200000000000007, 0.062000000000000055, 0.040000000000000036, 0.06600000000000006, 0.06400000000000006, 0.05800000000000005, 0.06600000000000006, 0.0020000000000000018, 0.1140000000000001, 0.0040000000000000036, 0.014000000000000012, 0.0040000000000000036, -0.03400000000000003, -0.03200000000000003, 0.026000000000000023, -0.0020000000000000018, -0.06000000000000005, 0.0040000000000000036, -0.018000000000000016, -0.04600000000000004, -0.008000000000000007, -0.014000000000000012, -0.06599999999999995, 0.006000000000000005, -0.006000000000000005, -0.038000000000000034, -0.0040000000000000036, 0.04400000000000004, 0.02400000000000002, 0.006000000000000005, -0.018000000000000016, -0.010000000000000009, -0.018000000000000016]\n"
     ]
    }
   ],
   "source": [
    "x_vals_control_off = []\n",
    "for i in range(41):\n",
    "    x_counts = marginal_counts(x_result.get_counts(i), meas_qubits=[1])\n",
    "    x_vals_control_off.append((x_counts.get('0', 0) / shots - 0.5) * 2)\n",
    "\n",
    "x_vals_control_on = []\n",
    "for i in range(41, 82):\n",
    "    x_counts = marginal_counts(x_result.get_counts(i), meas_qubits=[1])\n",
    "    x_vals_control_on.append((x_counts.get('0', 0) / shots - 0.5) * 2)\n",
    "\n",
    "print(x_vals_control_off)\n",
    "print(x_vals_control_on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.1259999999999999, 0.04400000000000004, -0.03400000000000003, -0.15800000000000003, -0.21599999999999997, -0.20799999999999996, -0.29800000000000004, -0.33799999999999997, -0.358, -0.43999999999999995, -0.516, -0.5760000000000001, -0.5660000000000001, -0.652, -0.6619999999999999, -0.6619999999999999, -0.74, -0.74, -0.748, -0.766, -0.764, -0.768, -0.742, -0.786, -0.764, -0.748, -0.716, -0.712, -0.6819999999999999, -0.65, -0.612, -0.548, -0.482, -0.496, -0.44199999999999995, -0.394, -0.378, -0.28, -0.21599999999999997, -0.21599999999999997, -0.052000000000000046]\n",
      "[0.21199999999999997, 0.28600000000000003, 0.3400000000000001, 0.45599999999999996, 0.494, 0.5940000000000001, 0.6120000000000001, 0.6000000000000001, 0.712, 0.7, 0.774, 0.784, 0.8520000000000001, 0.8680000000000001, 0.8620000000000001, 0.8899999999999999, 0.8979999999999999, 0.948, 0.9019999999999999, 0.9099999999999999, 0.9179999999999999, 0.9319999999999999, 0.8819999999999999, 0.8440000000000001, 0.8620000000000001, 0.8540000000000001, 0.802, 0.756, 0.71, 0.6679999999999999, 0.706, 0.6080000000000001, 0.5900000000000001, 0.486, 0.44799999999999995, 0.4159999999999999, 0.31400000000000006, 0.276, 0.22599999999999998, 0.21199999999999997, 0.1140000000000001]\n"
     ]
    }
   ],
   "source": [
    "y_vals_control_off = []\n",
    "for i in range(41):\n",
    "    y_counts = marginal_counts(y_result.get_counts(i), meas_qubits=[1])\n",
    "    y_vals_control_off.append((y_counts.get('0', 0) / shots - 0.5) * 2)\n",
    "\n",
    "y_vals_control_on = []\n",
    "for i in range(41, 82):\n",
    "    y_counts = marginal_counts(y_result.get_counts(i), meas_qubits=[1])\n",
    "    y_vals_control_on.append((y_counts.get('0', 0) / shots - 0.5) * 2)\n",
    "\n",
    "print(y_vals_control_off)\n",
    "print(y_vals_control_on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.972, 0.972, 0.968, 0.944, 0.9139999999999999, 0.8999999999999999, 0.8420000000000001, 0.8, 0.772, 0.782, 0.698, 0.5980000000000001, 0.6100000000000001, 0.5640000000000001, 0.558, 0.44199999999999995, 0.3779999999999999, 0.3700000000000001, 0.246, 0.20799999999999996, 0.18599999999999994, 0.05800000000000005, 0.02400000000000002, -0.09199999999999997, -0.132, -0.21599999999999997, -0.18999999999999995, -0.31200000000000006, -0.35, -0.372, -0.46199999999999997, -0.482, -0.5680000000000001, -0.558, -0.6719999999999999, -0.6719999999999999, -0.6599999999999999, -0.714, -0.728, -0.794, -0.84]\n",
      "[0.966, 0.952, 0.944, 0.8999999999999999, 0.8779999999999999, 0.806, 0.764, 0.744, 0.736, 0.6599999999999999, 0.6339999999999999, 0.5720000000000001, 0.504, 0.48, 0.3839999999999999, 0.3740000000000001, 0.32200000000000006, 0.17999999999999994, 0.1160000000000001, 0.050000000000000044, 0.04600000000000004, -0.02400000000000002, -0.050000000000000044, -0.08599999999999997, -0.19799999999999995, -0.244, -0.348, -0.406, -0.44999999999999996, -0.46599999999999997, -0.526, -0.554, -0.622, -0.6619999999999999, -0.6759999999999999, -0.732, -0.75, -0.78, -0.8200000000000001, -0.802, -0.784]\n"
     ]
    }
   ],
   "source": [
    "z_vals_control_off = []\n",
    "for i in range(41):\n",
    "    z_counts = marginal_counts(z_result.get_counts(i), meas_qubits=[1])\n",
    "    z_vals_control_off.append((z_counts.get('0', 0) / shots - 0.5) * 2)\n",
    "\n",
    "z_vals_control_on = []\n",
    "for i in range(41, 82):\n",
    "    z_counts = marginal_counts(z_result.get_counts(i), meas_qubits=[1])\n",
    "    z_vals_control_on.append((z_counts.get('0', 0) / shots - 0.5) * 2)\n",
    "\n",
    "print(z_vals_control_off)\n",
    "print(z_vals_control_on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_vals_control_off = [0.0020000000000000018, -0.02400000000000002, 0.03600000000000003, 0.028000000000000025, 0.02400000000000002, 0.06800000000000006, 0.07800000000000007, 0.016000000000000014, 0.04400000000000004, 0.0, 0.1319999999999999, 0.10200000000000009, 0.08000000000000007, 0.06600000000000006, 0.040000000000000036, 0.08200000000000007, 0.040000000000000036, 0.10400000000000009, 0.07800000000000007, 0.014000000000000012, 0.050000000000000044, -0.020000000000000018, 0.008000000000000007, 0.030000000000000027, 0.08400000000000007, 0.006000000000000005, 0.06400000000000006, 0.04200000000000004, 0.07800000000000007, 0.07800000000000007, 0.06000000000000005, 0.06000000000000005, 0.028000000000000025, 0.1200000000000001, 0.01200000000000001, 0.050000000000000044, 0.03400000000000003, 0.062000000000000055, -0.0020000000000000018, -0.0020000000000000018, -0.05600000000000005]\n",
    "x_vals_control_on = [0.028000000000000025, 0.05400000000000005, 0.04200000000000004, -0.020000000000000018, 0.0, 0.05600000000000005, 0.0, 0.08600000000000008, 0.08200000000000007, 0.062000000000000055, 0.040000000000000036, 0.06600000000000006, 0.06400000000000006, 0.05800000000000005, 0.06600000000000006, 0.0020000000000000018, 0.1140000000000001, 0.0040000000000000036, 0.014000000000000012, 0.0040000000000000036, -0.03400000000000003, -0.03200000000000003, 0.026000000000000023, -0.0020000000000000018, -0.06000000000000005, 0.0040000000000000036, -0.018000000000000016, -0.04600000000000004, -0.008000000000000007, -0.014000000000000012, -0.06599999999999995, 0.006000000000000005, -0.006000000000000005, -0.038000000000000034, -0.0040000000000000036, 0.04400000000000004, 0.02400000000000002, 0.006000000000000005, -0.018000000000000016, -0.010000000000000009, -0.018000000000000016]\n",
    "y_vals_control_off = [0.1259999999999999, 0.04400000000000004, -0.03400000000000003, -0.15800000000000003, -0.21599999999999997, -0.20799999999999996, -0.29800000000000004, -0.33799999999999997, -0.358, -0.43999999999999995, -0.516, -0.5760000000000001, -0.5660000000000001, -0.652, -0.6619999999999999, -0.6619999999999999, -0.74, -0.74, -0.748, -0.766, -0.764, -0.768, -0.742, -0.786, -0.764, -0.748, -0.716, -0.712, -0.6819999999999999, -0.65, -0.612, -0.548, -0.482, -0.496, -0.44199999999999995, -0.394, -0.378, -0.28, -0.21599999999999997, -0.21599999999999997, -0.052000000000000046]\n",
    "y_vals_control_on = [0.21199999999999997, 0.28600000000000003, 0.3400000000000001, 0.45599999999999996, 0.494, 0.5940000000000001, 0.6120000000000001, 0.6000000000000001, 0.712, 0.7, 0.774, 0.784, 0.8520000000000001, 0.8680000000000001, 0.8620000000000001, 0.8899999999999999, 0.8979999999999999, 0.948, 0.9019999999999999, 0.9099999999999999, 0.9179999999999999, 0.9319999999999999, 0.8819999999999999, 0.8440000000000001, 0.8620000000000001, 0.8540000000000001, 0.802, 0.756, 0.71, 0.6679999999999999, 0.706, 0.6080000000000001, 0.5900000000000001, 0.486, 0.44799999999999995, 0.4159999999999999, 0.31400000000000006, 0.276, 0.22599999999999998, 0.21199999999999997, 0.1140000000000001]\n",
    "z_vals_control_off = [0.972, 0.972, 0.968, 0.944, 0.9139999999999999, 0.8999999999999999, 0.8420000000000001, 0.8, 0.772, 0.782, 0.698, 0.5980000000000001, 0.6100000000000001, 0.5640000000000001, 0.558, 0.44199999999999995, 0.3779999999999999, 0.3700000000000001, 0.246, 0.20799999999999996, 0.18599999999999994, 0.05800000000000005, 0.02400000000000002, -0.09199999999999997, -0.132, -0.21599999999999997, -0.18999999999999995, -0.31200000000000006, -0.35, -0.372, -0.46199999999999997, -0.482, -0.5680000000000001, -0.558, -0.6719999999999999, -0.6719999999999999, -0.6599999999999999, -0.714, -0.728, -0.794, -0.84]\n",
    "z_vals_control_on = [0.966, 0.952, 0.944, 0.8999999999999999, 0.8779999999999999, 0.806, 0.764, 0.744, 0.736, 0.6599999999999999, 0.6339999999999999, 0.5720000000000001, 0.504, 0.48, 0.3839999999999999, 0.3740000000000001, 0.32200000000000006, 0.17999999999999994, 0.1160000000000001, 0.050000000000000044, 0.04600000000000004, -0.02400000000000002, -0.050000000000000044, -0.08599999999999997, -0.19799999999999995, -0.244, -0.348, -0.406, -0.44999999999999996, -0.46599999999999997, -0.526, -0.554, -0.622, -0.6619999999999999, -0.6759999999999999, -0.732, -0.75, -0.78, -0.8200000000000001, -0.802, -0.784]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "thetas = np.linspace(0, 180, 41, endpoint=True)\n",
    "dense_thetas = np.linspace(0, 180, 101, endpoint=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.rcParams.update({'font.size': 14})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x180 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 2.5))\n",
    "plt.plot(thetas, x_vals_control_off, label='Control |0>', linewidth=3)\n",
    "plt.plot(thetas, x_vals_control_on, label='Control |1>', linewidth=3)\n",
    "plt.plot(dense_thetas, [0] * 101, label='Ideal', color='black', linestyle='dashed', linewidth=3)\n",
    "plt.axis([0, 180, -1, 1])\n",
    "plt.yticks([-1, -.5, 0, .5, 1])\n",
    "plt.xticks([0, 45, 90, 135, 180])\n",
    "plt.legend(mode='expand', ncol=2, loc=2)\n",
    "plt.ylabel('<X> on Target')\n",
    "plt.savefig('Figures/CR_X.svg', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 2))\n",
    "plt.plot(thetas, y_vals_control_off, label='Control |0>', linewidth=3)\n",
    "plt.plot(thetas, y_vals_control_on, label='Control |1>', linewidth=3)\n",
    "plt.plot(dense_thetas, np.sin(dense_thetas*np.pi/180), label='Ideal', color='black', linestyle='dashed', linewidth=3)\n",
    "plt.plot(dense_thetas, -np.sin(dense_thetas*np.pi/180), label='Ideal', color='black', linestyle='dashed', linewidth=3)\n",
    "plt.axis([0, 180, -1, 1])\n",
    "plt.ylabel('<Y> on Target')\n",
    "plt.yticks([-1, -.5, 0, .5, 1])\n",
    "_=plt.xticks([0, 45, 90, 135, 180])\n",
    "plt.savefig('Figures/CR_Y.svg', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x172.8 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(6, 2.4))\n",
    "plt.plot(thetas, z_vals_control_off, label='Control |0>', linewidth=3)\n",
    "plt.plot(thetas, z_vals_control_on, label='Control |1>', linewidth=3)\n",
    "plt.plot(dense_thetas, np.cos(dense_thetas*np.pi/180), label='Ideal', color='black', linestyle='dashed', linewidth=3)\n",
    "plt.axis([0, 180, -1, 1])\n",
    "plt.ylabel('<Z> on Target')\n",
    "plt.yticks([-1, -.5, 0, .5, 1])\n",
    "_=plt.xticks([0, 45, 90, 135, 180])\n",
    "plt.xlabel('Θ in degrees')\n",
    "plt.gcf().subplots_adjust(bottom=.2)\n",
    "plt.savefig('Figures/CR_Z.svg', bbox_inches = \"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}