{ "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": [ "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wr.print_eng_file(jup=True)" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "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": [ "
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ROTY\t25.714286\tAT\t2
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ROTX\t#2*.5\tAT\t1
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ROTX\t-#1*.3\tAT\t1
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SIGX\tAT\t3\tIF\t2T
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SWAP\t2\t1
" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "wr.print_eng_file(jup=True)" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "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')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.10.9" }, "toc": { "colors": { "hover_highlight": "#DAA520", "running_highlight": "#FF0000", "selected_highlight": "#FFD700" }, "moveMenuLeft": true, "nav_menu": { "height": "12px", "width": "252px" }, "navigate_menu": true, "number_sections": true, "sideBar": true, "threshold": 4, "toc_cell": false, "toc_section_display": "block", "toc_window_display": false, "widenNotebook": false } }, "nbformat": 4, "nbformat_minor": 4 }