{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MeanHamilMinimizer, native with Autograd\n",
"* Feedback loop between Qubiter and Qubiter\n",
"* minimization via autograd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## First Example (taken from Pennylane docs). "
]
},
{
"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.MeanHamilMinimizer import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"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*.5')\n",
"wr.close_files()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| 1 | ROTX\t#1\tAT\t0 |
2 | ROTY\t-#2*.5\tAT\t0 |
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wr.print_eng_file(jup=True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
""
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wr.print_pic_file(jup=True)"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"fun_name_to_fun = None"
]
},
{
"cell_type": "code",
"execution_count": 7,
"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": 8,
"metadata": {},
"outputs": [],
"source": [
"init_var_num_to_rads = {1: .3, 2: .8}\n",
"all_var_nums = [1, 2]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"num_samples = 0\n",
"print_hiatus = 4\n",
"verbose = False\n",
"np.random.seed(1234)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"emp_mhamil = MeanHamil_native(file_prefix, num_qbits, hamil,\n",
" all_var_nums, fun_name_to_fun, simulator_name='SEO_simulator', 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": 11,
"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": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_val~ (#1, #2)\n",
"iter=0, cost=0.575017, targ_cost=0.575017, x_val=0.300000, 0.800000\n",
"iter=4, cost=0.123982, targ_cost=0.123982, x_val=0.678570, 0.946258\n",
"iter=8, cost=-0.434184, targ_cost=-0.434184, x_val=1.138412, 0.837671\n",
"iter=12, cost=-0.804328, targ_cost=-0.804328, x_val=1.451798, 0.595875\n",
"iter=16, cost=-0.920328, targ_cost=-0.920328, x_val=1.549012, 0.399637\n",
"iter=20, cost=-0.965189, targ_cost=-0.965189, x_val=1.567496, 0.264552\n",
"iter=24, cost=-0.984857, targ_cost=-0.984857, x_val=1.570338, 0.174248\n",
"iter=28, cost=-0.993450, targ_cost=-0.993450, x_val=1.570735, 0.114517\n",
"iter=32, cost=-0.997175, targ_cost=-0.997175, x_val=1.570788, 0.075189\n",
"iter=36, cost=-0.998783, targ_cost=-0.998783, x_val=1.570795, 0.049347\n"
]
}
],
"source": [
"mini.find_min(minlib='autograd', num_iter=40, descent_rate=.1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Second, more complicated example"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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_Ry(1, rads='#2')\n",
"wr.write_Rx(1, rads='#1')\n",
"wr.write_cnot(2, 3)\n",
"wr.write_qbit_swap(1, 2)\n",
"wr.close_files()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| 1 | ROTY\t25.714286\tAT\t2 |
2 | ROTY\t#2\tAT\t1 | 3 | ROTX\t#1\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": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"| 1 | | Ry | | |
2 | | | Ry | | 3 | | | Rx | | 4 | X---@ | | | 5 | | <---> | |
"
],
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"wr.print_pic_file(jup=True)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"fun_name_to_fun = None"
]
},
{
"cell_type": "code",
"execution_count": 17,
"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": 18,
"metadata": {},
"outputs": [],
"source": [
"init_var_num_to_rads = {1: 2.1, 2:1.2}\n",
"all_var_nums = [1, 2]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"num_samples = 0\n",
"print_hiatus = 2\n",
"verbose = False\n",
"np.random.seed(1234)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"emp_mhamil = MeanHamil_native(file_prefix, num_qbits, hamil,\n",
" all_var_nums, fun_name_to_fun, simulator_name='SEO_simulator', 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": 21,
"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": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"x_val~ (#1, #2)\n",
"iter=0, cost=-0.211239, targ_cost=-0.211239, x_val=2.100000, 1.200000\n",
"iter=2, cost=-0.248413, targ_cost=-0.248413, x_val=2.100000, 1.111845\n",
"iter=4, cost=-0.273138, targ_cost=-0.273138, x_val=2.100000, 1.039734\n",
"iter=6, cost=-0.288820, targ_cost=-0.288820, x_val=2.100000, 0.982194\n",
"iter=8, cost=-0.298464, targ_cost=-0.298464, x_val=2.100000, 0.937017\n",
"iter=10, cost=-0.304281, targ_cost=-0.304281, x_val=2.100000, 0.901905\n",
"iter=12, cost=-0.307749, targ_cost=-0.307749, x_val=2.100000, 0.874784\n",
"iter=14, cost=-0.309801, targ_cost=-0.309801, x_val=2.100000, 0.853911\n",
"iter=16, cost=-0.311011, targ_cost=-0.311011, x_val=2.100000, 0.837884\n",
"iter=18, cost=-0.311723, targ_cost=-0.311723, x_val=2.100000, 0.825593\n"
]
}
],
"source": [
"mini.find_min(minlib='autograd', num_iter=20, descent_rate=.1)"
]
}
],
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"display_name": "Python 3 (ipykernel)",
"language": "python",
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"hover_highlight": "#DAA520",
"running_highlight": "#FF0000",
"selected_highlight": "#FFD700"
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"moveMenuLeft": true,
"nav_menu": {
"height": "12px",
"width": "252px"
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"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 4,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false,
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"nbformat_minor": 4
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