qubiter/jupyter_notebooks/MeanHamilMinimizer_rigetti_scipy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MeanHamilMinimizer_rigetti_scipy\n",
    "* Feedback loop between Qubiter and Rigetti QVM\n",
    "* minimization via scipy.optimize.minimize\n",
    "\n",
    ">This notebook calls Rigetti's method QVMConnection() which only works if you first:\n",
    "* install the Rigetti Forest SDK available at https://www.rigetti.com/forest\n",
    "* open a second terminal (besides the one that runs this notebook) and type \"qvm -S\" in it\n",
    "* open a third terminal and type \"quilc -S\" in it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/rrtucci/PycharmProjects/qubiter/qubiter/jupyter_notebooks\n",
      "/home/rrtucci/PycharmProjects/qubiter\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import sys\n",
    "print(os.getcwd())\n",
    "os.chdir('../../')\n",
    "print(os.getcwd())\n",
    "sys.path.insert(0,os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "loaded OneQubitGate, WITHOUT autograd.numpy\n"
     ]
    }
   ],
   "source": [
    "from qubiter.adv_applications.MeanHamil_native import *\n",
    "from qubiter.adv_applications.MeanHamil_rigetti import *\n",
    "from qubiter.adv_applications.MeanHamilMinimizer import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pyquil.quil import Program\n",
    "from pyquil.api import QVMConnection\n",
    "from pyquil.gates import *\n",
    "from pyquil import get_qc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "qvm_url = \"http://localhost:5000\"\n",
    "compiler_server_address = \"tcp://localhost:5555\"\n",
    "forest_url = \"https://forest-server.qcs.rigetti.com\"\n",
    "qvm = QVMConnection(endpoint=qvm_url, compiler_endpoint=compiler_server_address)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Example (taken from Pennylane docs). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "qc = get_qc('2q-qvm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_qbits = 2\n",
    "file_prefix = 'mean_hamil_rigetti_test1'\n",
    "emb = CktEmbedder(num_qbits, num_qbits)\n",
    "wr = SEO_writer(file_prefix, emb)\n",
    "wr.write_Rx(0, rads='#1')\n",
    "wr.write_Ry(0, rads='#2')\n",
    "wr.close_files()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='font-family:monospace'><tr><td style='border-right:1px solid red;'>1</td><td style='text-align:left;'><pre>ROTX\t#1\tAT\t0</pre></td></tr><td style='border-right:1px solid red;'>2</td><td style='text-align:left;'><pre>ROTY\t#2\tAT\t0</pre></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wr.print_eng_file(jup=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='font-family:monospace'><tr><td style='border-right:1px solid red;'>1</td><td style='text-align:left;'><pre>|   Rx</pre></td></tr><td style='border-right:1px solid red;'>2</td><td style='text-align:left;'><pre>|   Ry</pre></td></tr></table>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wr.print_pic_file(jup=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "fun_name_to_fun = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hamil=\n",
      " 1.0 [Z0]\n"
     ]
    }
   ],
   "source": [
    "hamil = QubitOperator('Z0', 1.)\n",
    "print('hamil=\\n', hamil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_var_num_to_rads = {1: .3, 2: .8}\n",
    "all_var_nums = [1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = 100\n",
    "rand_seed = 1234\n",
    "print_hiatus = 10\n",
    "verbose = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = 100\n",
    "print_hiatus = 10\n",
    "verbose = False\n",
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_mhamil = MeanHamil_rigetti(qc, file_prefix, num_qbits, hamil,\n",
    "            all_var_nums, fun_name_to_fun, num_samples=num_samples)\n",
    "targ_mhamil = MeanHamil_native(file_prefix, num_qbits, hamil,\n",
    "            all_var_nums, fun_name_to_fun, simulator_name='SEO_simulator') # zero samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "mini = MeanHamilMinimizer(emp_mhamil, targ_mhamil,\n",
    "                 all_var_nums, init_var_num_to_rads,\n",
    "                 print_hiatus=print_hiatus, verbose=verbose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pg += RX(rads1*(-2), 0)\n",
      "pg += RY(rads2*(-2), 0)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "emp_mhamil.translator.print_aqasm_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRAGMA INITIAL_REWIRING \"PARTIAL\"\n",
      "RESET\n",
      "DECLARE ro BIT[2]\n",
      "DECLARE rads1 REAL[1]\n",
      "DECLARE rads2 REAL[1]\n",
      "RX(rads1*-2) 0\n",
      "RY(rads2*-2) 0\n",
      "MEASURE 0 ro[0]\n",
      "MEASURE 1 ro[1]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(emp_mhamil.pg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_val~ (#1, #2)\n",
      "iter=0, cost=-0.020000, x_val=0.300000, 0.800000\n",
      "iter=10, cost=0.100000, x_val=-0.609830, 0.800000\n",
      "iter=20, cost=-0.360000, x_val=-0.575388, 1.418034\n",
      "iter=30, cost=-0.620000, x_val=-2.867184, 4.381854\n",
      "iter=40, cost=0.920000, x_val=-3.260178, 2.996423\n",
      "iter=50, cost=-0.940000, x_val=-1.645093, 2.996423\n",
      "iter=60, cost=-0.980000, x_val=-1.642198, 2.996423\n",
      "iter=70, cost=-0.960000, x_val=-1.642141, 2.996423\n",
      "iter=80, cost=-0.560000, x_val=-2.049606, 3.457236\n",
      "iter=90, cost=-0.880000, x_val=-1.630944, 2.983760\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   direc: array([[ 1.        ,  0.        ],\n",
       "       [-1.06675607,  1.20642308]])\n",
       "     fun: -0.98\n",
       " message: 'Optimization terminated successfully.'\n",
       "    nfev: 95\n",
       "     nit: 2\n",
       "  status: 0\n",
       " success: True\n",
       "       x: array([-1.63027098,  2.98299858])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mini.find_min(minlib='scipy', method='Powell')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Second, more complicated example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "qc = get_qc('4q-qvm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_qbits = 4\n",
    "file_prefix = 'mean_hamil_rigetti_test2'\n",
    "emb = CktEmbedder(num_qbits, num_qbits)\n",
    "wr = SEO_writer(file_prefix, emb)\n",
    "wr.write_Ry(2, rads=np.pi/7)\n",
    "wr.write_Rx(1, rads='#2*.5')\n",
    "# wr.write_Rn(3, rads_list=['#1', '-#1*3', '#2'])\n",
    "wr.write_Rx(1, rads='-#1*.3')\n",
    "wr.write_cnot(2, 3)\n",
    "wr.write_qbit_swap(1, 2)\n",
    "wr.close_files()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='font-family:monospace'><tr><td style='border-right:1px solid red;'>1</td><td style='text-align:left;'><pre>ROTY\t25.714286\tAT\t2</pre></td></tr><td style='border-right:1px solid red;'>2</td><td style='text-align:left;'><pre>ROTX\t#2*.5\tAT\t1</pre></td></tr><td style='border-right:1px solid red;'>3</td><td style='text-align:left;'><pre>ROTX\t-#1*.3\tAT\t1</pre></td></tr><td style='border-right:1px solid red;'>4</td><td style='text-align:left;'><pre>SIGX\tAT\t3\tIF\t2T</pre></td></tr><td style='border-right:1px solid red;'>5</td><td style='text-align:left;'><pre>SWAP\t2\t1</pre></td></tr></table>"
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       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
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   ],
   "source": [
    "wr.print_eng_file(jup=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style='font-family:monospace'><tr><td style='border-right:1px solid red;'>1</td><td style='text-align:left;'><pre>|   Ry  |   |</pre></td></tr><td style='border-right:1px solid red;'>2</td><td style='text-align:left;'><pre>|   |   Rx  |</pre></td></tr><td style='border-right:1px solid red;'>3</td><td style='text-align:left;'><pre>|   |   Rx  |</pre></td></tr><td style='border-right:1px solid red;'>4</td><td style='text-align:left;'><pre>X---@   |   |</pre></td></tr><td style='border-right:1px solid red;'>5</td><td style='text-align:left;'><pre>|   <--->   |</pre></td></tr></table>"
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       "<IPython.core.display.HTML object>"
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "wr.print_pic_file(jup=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "fun_name_to_fun = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hamil=\n",
      " 0.7 [X1 Y2] +\n",
      "0.4 [Y1 Y3]\n"
     ]
    }
   ],
   "source": [
    "hamil = QubitOperator('X1 Y3 X1 Y1', .4) + QubitOperator('Y2 X1', .7)\n",
    "print('hamil=\\n', hamil)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "init_var_num_to_rads = {1: 2.1, 2: 3.4}\n",
    "all_var_nums = [1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_samples = 100\n",
    "print_hiatus = 25\n",
    "verbose = False\n",
    "np.random.seed(1234)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "emp_mhamil = MeanHamil_rigetti(qc, file_prefix, num_qbits, hamil,\n",
    "            all_var_nums, fun_name_to_fun, num_samples=num_samples)\n",
    "targ_mhamil = MeanHamil_native(file_prefix, num_qbits, hamil,\n",
    "            all_var_nums, fun_name_to_fun, simulator_name='SEO_simulator') # zero samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "mini = MeanHamilMinimizer(emp_mhamil, targ_mhamil,\n",
    "                 all_var_nums, init_var_num_to_rads,\n",
    "                 print_hiatus=print_hiatus, verbose=verbose)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "pg += RY(-0.8975979109989651, 2)\n",
      "pg += RX(rads2*.5*(-2), 1)\n",
      "pg += RX(-rads1*.3*(-2), 1)\n",
      "pg += CNOT(2, 3)\n",
      "pg += SWAP(2, 1)\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "emp_mhamil.translator.print_aqasm_file()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "PRAGMA INITIAL_REWIRING \"PARTIAL\"\n",
      "RESET\n",
      "DECLARE ro BIT[4]\n",
      "DECLARE rads1 REAL[1]\n",
      "DECLARE rads2 REAL[1]\n",
      "RY(-0.8975979109989651) 2\n",
      "RX(rads2*0.5*-2) 1\n",
      "RX(-1*rads1*0.3*-2) 1\n",
      "CNOT 2 3\n",
      "SWAP 2 1\n",
      "RY(-pi/2) 1\n",
      "RX(pi/2) 2\n",
      "MEASURE 0 ro[0]\n",
      "MEASURE 1 ro[1]\n",
      "MEASURE 2 ro[2]\n",
      "MEASURE 3 ro[3]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(emp_mhamil.pg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_val~ (#1, #2)\n",
      "iter=0, cost=0.340000, x_val=2.100000, 3.400000\n",
      "iter=25, cost=0.340000, x_val=3.969473, 3.537767\n",
      "iter=50, cost=0.232000, x_val=5.014259, 2.549414\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "   direc: array([[1., 0.],\n",
       "       [0., 1.]])\n",
       "     fun: 0.23199999999999998\n",
       " message: 'Optimization terminated successfully.'\n",
       "    nfev: 59\n",
       "     nit: 2\n",
       "  status: 0\n",
       " success: True\n",
       "       x: array([5.01425901, 2.54941381])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mini.find_min(minlib='scipy', method='Powell')"
   ]
  }
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