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fixing up maxcut notebook #218

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fixing up maxcut notebook #218

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73bdf42
fixing up maxcut notebook
Dec 3, 2019
091470f
updates to notebook
Jan 12, 2020
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148 changes: 64 additions & 84 deletions grove/pyqaoa/getting-started/QAOA_overview_maxcut.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -26,12 +26,9 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"import pyquil.forest as qvm_module\n",
"import numpy as np\n",
"from grove.pyqaoa.maxcut_qaoa import maxcut_qaoa\n",
"barbell = [(0, 1)] # graph is a defined by a list of edges. Edge weights are assumed to be 1.0\n",
Expand All @@ -49,16 +46,15 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from functools import reduce\n",
"cost_list, ref_list = inst.cost_ham, inst.ref_ham\n",
"cost_ham = reduce(lambda x,y: x + y, cost_list)\n",
"ref_ham = reduce(lambda x,y: x + y, ref_list)\n",
"print cost_ham\n",
"print ref_ham"
"print(cost_ham)\n",
"print(ref_ham)"
]
},
{
Expand Down Expand Up @@ -87,14 +83,12 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"param_prog = inst.get_parameterized_program()\n",
"prog = param_prog([1.2, 4.2])\n",
"print prog"
"print(prog)"
]
},
{
Expand All @@ -107,13 +101,11 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"betas, gammas = inst.get_angles()\n",
"print betas, gammas"
"print(betas, gammas)"
]
},
{
Expand All @@ -126,18 +118,19 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from pyquil.api import WavefunctionSimulator\n",
"\n",
"param_prog = inst.get_parameterized_program()\n",
"t = np.hstack((betas, gammas))\n",
"prog = param_prog(t)\n",
"wf, _ = inst.qvm.wavefunction(prog)\n",
"\n",
"wf = WavefunctionSimulator().wavefunction(prog)\n",
"wf = wf.amplitudes\n",
"for ii in range(2**inst.n_qubits):\n",
" print inst.states[ii], np.conj(wf[ii])*wf[ii]"
"for ii in range(2**len(inst.qubits)):\n",
" print(inst.states[ii], np.conj(wf[ii])*wf[ii])"
]
},
{
Expand All @@ -164,9 +157,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
Expand All @@ -176,17 +167,13 @@
"import matplotlib.pyplot as plt\n",
"from pyquil.paulis import PauliSum, PauliTerm\n",
"import pyquil.quil as pq\n",
"from pyquil.gates import H\n",
"import pyquil.forest as qvm_module\n",
"CXN = qvm_module.Connection()"
"from pyquil.gates import H"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# define a 6-qubit ring\n",
Expand All @@ -199,9 +186,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"nx.draw_circular(graph, node_color=\"#6CAFB7\")"
Expand All @@ -224,9 +209,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"cost_operators = []\n",
Expand All @@ -247,9 +230,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"prog = pq.Program()\n",
Expand All @@ -267,21 +248,21 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"ring_cut_inst = QAOA(CXN, len(graph.nodes()), steps=1, ref_ham=driver_operators, cost_ham=cost_operators,\n",
" driver_ref=prog, store_basis=True, rand_seed=42)"
"from pyquil.api import local_forest_runtime\n",
"from pyquil import get_qc, Program\n",
"with local_forest_runtime():\n",
" qvm = get_qc('6q-qvm')\n",
" ring_cut_inst = QAOA(qvm, range(len(graph.nodes())), steps=1, ref_ham=driver_operators, cost_ham=cost_operators,\n",
" driver_ref=prog, store_basis=True, rand_seed=42)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"betas, gammas = ring_cut_inst.get_angles()"
Expand All @@ -297,9 +278,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"from collections import Counter\n",
Expand All @@ -309,15 +288,18 @@
"sampling_prog = param_prog(np.hstack((betas, gammas)))\n",
"\n",
"# use the run_and_measure QVM API to prepare a circuit and then measure on the qubits\n",
"bitstring_samples = CXN.run_and_measure(sampling_prog, range(len(graph.nodes())), 1000)\n",
"bitstring_tuples = map(tuple, bitstring_samples)\n",
"with local_forest_runtime():\n",
" qvm = get_qc('6q-qvm')\n",
" bitstring_samples = qvm.run_and_measure(sampling_prog, 1000)\n",
" shots = np.array([bitstring_samples[qubit] for qubit in sorted(bitstring_samples)]).T\n",
" bitstring_tuples = map(tuple, shots)\n",
"\n",
"# aggregate the statistics\n",
"freq = Counter(bitstring_tuples)\n",
"most_frequent_bit_string = max(freq, key=lambda x: freq[x])\n",
"print freq\n",
"print(freq)\n",
"\n",
"print \"The most frequently sampled string is \", most_frequent_bit_string"
"print(\"The most frequently sampled string is \", most_frequent_bit_string)"
]
},
{
Expand All @@ -330,9 +312,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# plotting strings!\n",
Expand All @@ -345,9 +325,9 @@
" ax.set_xlabel(\"state\",fontsize=20)\n",
" ax.set_ylabel(\"Probability\",fontsize=20)\n",
" ax.set_xlim([0, 2**n_qubits ])\n",
" rec = ax.bar(range(2**n_qubits), probs, )\n",
" num_states = [0, int(\"\".join(str(x) for x in [0,1] * (n_qubits/2)), 2), \n",
" int(\"\".join(str(x) for x in [1,0] * (n_qubits/2)), 2), 2**n_qubits - 1 ]\n",
" rec = ax.bar(range(2**n_qubits),height=list(probs.T[0]))\n",
" num_states = [0, int(\"\".join(str(x) for x in [0,1] * int(n_qubits/2)), 2), \n",
" int(\"\".join(str(x) for x in [1,0] * int(n_qubits/2)), 2), 2**n_qubits - 1 ]\n",
" ax.set_xticks(num_states)\n",
" ax.set_xticklabels(map(lambda x: inst.states[x], num_states), rotation=90)\n",
" plt.grid(True)\n",
Expand All @@ -358,9 +338,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"t = np.hstack((betas, gammas))\n",
Expand All @@ -378,9 +356,7 @@
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"metadata": {},
"outputs": [],
"source": [
"# get the angles from the last run\n",
Expand All @@ -390,8 +366,10 @@
"betas = np.hstack((beta[0]/3, beta[0]*2/3))\n",
"gammas = np.hstack((gamma[0]/3, gamma[0]*2/3))\n",
"# set up a new QAOA instance.\n",
"ring_cut_inst_2 = QAOA(CXN, len(graph.nodes()), steps=2, ref_ham=driver_operators, cost_ham=cost_operators,\n",
" driver_ref=prog, store_basis=True, init_betas=betas, init_gammas=gammas)\n",
"with local_forest_runtime():\n",
" qvm = get_qc('6q-qvm')\n",
" ring_cut_inst_2 = QAOA(qvm, range(len(graph.nodes())), steps=2, ref_ham=driver_operators, cost_ham=cost_operators,\n",
" driver_ref=prog, store_basis=True, init_betas=list(betas), init_gammas=list(gammas))\n",
"# run VQE to determine the optimal angles\n",
"betas, gammas = ring_cut_inst_2.get_angles()\n",
"t = np.hstack((betas, gammas))\n",
Expand All @@ -410,42 +388,44 @@
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
"scrolled": true
},
"outputs": [],
"source": [
"from scipy.optimize import fmin_bfgs\n",
"\n",
"ring_cut_inst_3 = QAOA(CXN, len(graph.nodes()), steps=3, ref_ham=driver_operators, cost_ham=cost_operators,\n",
" driver_ref=prog, store_basis=True, minimizer=fmin_bfgs, minimizer_kwargs={'gtol':1.0e-3},\n",
" rand_seed=42)\n",
"betas, gammas = ring_cut_inst_3.get_angles()\n",
"t = np.hstack((betas, gammas))\n",
"probs = ring_cut_inst_3.probabilities(t)\n",
"plot(ring_cut_inst_3, probs)"
"with local_forest_runtime():\n",
" qvm = get_qc('6q-qvm')\n",
" ring_cut_inst_3 = QAOA(qvm, range(len(graph.nodes())), steps=3, ref_ham=driver_operators, cost_ham=cost_operators,\n",
" driver_ref=prog, store_basis=True, minimizer=fmin_bfgs, minimizer_kwargs={'gtol':1.0e-3},\n",
" rand_seed=42)\n",
" betas, gammas = ring_cut_inst_3.get_angles()\n",
" t = np.hstack((betas, gammas))\n",
" probs = ring_cut_inst_3.probabilities(t)\n",
" plot(ring_cut_inst_3, probs)"
]
}
],
"metadata": {
"anaconda-cloud": {},
"kernelspec": {
"display_name": "Python [default]",
"display_name": "Python 3",
"language": "python",
"name": "python2"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
"pygments_lexer": "ipython3",
"version": "3.7.3"
}
},
"nbformat": 4,
"nbformat_minor": 0
"nbformat_minor": 1
}