{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MeanHamilMinimizer_rigetti_autograd\n",
"\n",
"* Feedback loop between Qubiter and Rigetti QVM\n",
"* minimization via autograd\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": [
"np installed? False\n",
"numpy installed? True\n",
"autograd.numpy installed? True\n",
"loaded OneQubitGate, WITH autograd.numpy\n",
"pu2 in dir True\n",
"pu2 in sys.modules False\n"
]
}
],
"source": [
"import qubiter.adv_applications.setup_autograd # do this first!\n",
"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": [
"
1 | ROTX\t#1\tAT\t0 |
2 | ROTY\t#2\tAT\t0 |
"
],
"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": [
""
],
"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",
"print_hiatus = 1\n",
"verbose = False\n",
"np.random.seed(1234)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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": 14,
"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": 15,
"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": 16,
"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": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_val~ (#1, #2)\n",
"iter=0, cost=-0.060000, targ_cost=-0.024099, x_val=0.300000, 0.800000\n",
"iter=1, cost=-0.340000, targ_cost=-0.292577, x_val=0.296703, 0.965738\n",
"iter=2, cost=-0.620000, targ_cost=-0.551784, x_val=0.257234, 1.128646\n",
"iter=3, cost=-0.680000, targ_cost=-0.764436, x_val=0.194856, 1.271741\n",
"iter=4, cost=-0.880000, targ_cost=-0.896213, x_val=0.132062, 1.380452\n",
"iter=5, cost=-0.960000, targ_cost=-0.959154, x_val=0.083588, 1.453728\n",
"iter=6, cost=-1.000000, targ_cost=-0.984771, x_val=0.051216, 1.499885\n",
"iter=7, cost=-1.000000, targ_cost=-0.994446, x_val=0.030971, 1.528101\n",
"iter=8, cost=-1.000000, targ_cost=-0.997991, x_val=0.018635, 1.545146\n",
"iter=9, cost=-1.000000, targ_cost=-0.999275, x_val=0.011193, 1.555399\n"
]
}
],
"source": [
"mini.find_min(minlib='autograd', num_iter=10, descent_rate=.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Second, more complicated example"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"qc = get_qc('4q-qvm')"
]
},
{
"cell_type": "code",
"execution_count": 19,
"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_Ry(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": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"1 | ROTY\t25.714286\tAT\t2 |
2 | ROTX\t#2*.5\tAT\t1 | 3 | ROTY\t-#1*.3\tAT\t1 | 4 | SIGX\tAT\t3\tIF\t2T | 5 | SWAP\t2\t1 |
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wr.print_eng_file(jup=True)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"1 | | Ry | | |
2 | | | Rx | | 3 | | | Ry | | 4 | X---@ | | | 5 | | <---> | |
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wr.print_pic_file(jup=True)"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"fun_name_to_fun = None"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"hamil=\n",
" 0.7 [X1 Y2] +\n",
"0.4 [Y1 X2 Y3]\n"
]
}
],
"source": [
"hamil = QubitOperator('X1 Y3 X1 Y1 X2', .4) + QubitOperator('Y2 X1', .7)\n",
"print('hamil=\\n', hamil)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"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": 25,
"metadata": {},
"outputs": [],
"source": [
"num_samples = 100\n",
"print_hiatus = 1\n",
"verbose = False\n",
"np.random.seed(1234)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"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": 27,
"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": 28,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n",
"pg += RY(-0.8975979109989651, 2)\n",
"pg += RX(rads2*.5*(-2), 1)\n",
"pg += RY(-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": 29,
"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",
"RY(-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": 30,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_val~ (#1, #2)\n",
"iter=0, cost=-0.336000, targ_cost=-0.287864, x_val=2.100000, 3.400000\n",
"iter=1, cost=-0.316000, targ_cost=-0.288742, x_val=2.105548, 3.392383\n",
"iter=2, cost=-0.370000, targ_cost=-0.289583, x_val=2.111049, 3.384978\n",
"iter=3, cost=-0.192000, targ_cost=-0.290390, x_val=2.116503, 3.377780\n",
"iter=4, cost=-0.246000, targ_cost=-0.291163, x_val=2.121910, 3.370784\n",
"iter=5, cost=-0.310000, targ_cost=-0.291904, x_val=2.127269, 3.363985\n",
"iter=6, cost=-0.224000, targ_cost=-0.292615, x_val=2.132580, 3.357378\n",
"iter=7, cost=-0.426000, targ_cost=-0.293297, x_val=2.137843, 3.350958\n",
"iter=8, cost=-0.170000, targ_cost=-0.293951, x_val=2.143058, 3.344720\n",
"iter=9, cost=-0.274000, targ_cost=-0.294578, x_val=2.148225, 3.338660\n"
]
}
],
"source": [
"mini.find_min(minlib='autograd', num_iter=10, descent_rate=.1)"
]
}
],
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