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maxCliqueQAOA.py
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maxCliqueQAOA.py
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import pennylane as qml
from pennylane import numpy as np
from matplotlib import pyplot as plt
from collections import Counter
import networkx as nx
import csv
seeds = [42]
graphs_and_wires = []
# solution: 11100
edges = [(0, 1), (1, 2), (2, 0), (2, 3), (1, 4)]
graphs_and_wires.append((nx.Graph(edges), 5))
# # solution: 1110000
# edges = [(0, 1), (1, 2), (2, 0), (2, 3), (1, 4), (4, 5), (5, 2), (0, 6)]
# graphs_and_wires.append((nx.Graph(edges), 7))
#
# # solution: 1110010
# edges = [(0, 1), (1, 2), (2, 0), (2, 3), (1, 4), (4, 5), (5, 2), (0, 6), (0, 5), (1, 5)]
# graphs_and_wires.append((nx.Graph(edges), 7))
#
# # solution: 1110
# edges = [(0, 1), (0, 2), (0, 3), (1, 2)]
# graphs_and_wires.append((nx.Graph(edges), 4))
#
# # solution: 1111
# edges = [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3), (2, 3)]
# graphs_and_wires.append((nx.Graph(edges), 4))
for index in range(len(graphs_and_wires)):
graph, n_wires = graphs_and_wires[index]
for seed in seeds:
np.random.seed(seed)
cost_h, mixer_h = qml.qaoa.max_clique(graph, constrained=False)
# print("cost_h:")
# print(cost_h)
# print("mixer_h:")
# print(mixer_h)
shots = 1
dev = qml.device("default.qubit", wires=n_wires, shots=shots)
def bitstring_to_int(bit_string_sample):
bit_string = "".join(str(bs) for bs in bit_string_sample)
return int(bit_string, base=2)
def bitarray_Z_to_int(bit_array_sample):
s = bit_array_sample.T
s = (1 - s.numpy()) / 2
bit_string = "".join(str(int(bs)) for bs in s[0])
return int(bit_string, base=2)
@qml.qnode(dev)
def circuit(gammas, betas, sample=False, n_layers=1):
# apply Hadamards to get the n qubit |+> state
for wire in range(n_wires):
qml.Hadamard(wires=wire)
# p instances of unitary operators
for i in range(n_layers):
qml.templates.ApproxTimeEvolution(cost_h, gammas[i], 1)
qml.templates.ApproxTimeEvolution(mixer_h, betas[i], 1)
if sample:
# measurement phase
return [qml.sample(qml.PauliZ(i)) for i in range(n_wires)]
# during the optimization phase we are evaluating a term
# in the objective using expval
return qml.expval(cost_h)
def qaoa_maxclique(n_layers=1):
print("\np={:d}".format(n_layers))
f = open('/home/jockel/master_2/QAOA/angles_graph_' + str(index) + '_seed_' + str(seed) + '_layers_' + str(
n_layers) + '.csv', 'w')
writer = csv.writer(f)
row = ['step']
for i in range(n_layers):
row.append('gamma' + str(i))
row.append('beta' + str(i))
writer.writerow(row)
# initialize the parameters near zero
init_params = np.random.rand(2, n_layers)
# minimize the negative of the objective function
def objective(params):
gammas = params[0]
betas = params[1]
obj = circuit(gammas, betas, sample=False, n_layers=n_layers)
return obj
# initialize optimizer: Adagrad works well empirically
# TODO: optimizer = qml.GradientDescentOptimizer()
opt = qml.GradientDescentOptimizer()
# optimize parameters in objective
params = init_params
steps = 50
for i in range(steps):
params = opt.step(objective, params)
row = [i]
for l in range(n_layers):
row.append(params[0][l])
row.append(params[1][l])
writer.writerow(row)
if (i + 1) % 10 == 0:
print("Objective after step {:5d}: {: .7f}".format(i + 1, -objective(params)))
# sample measured bitstrings 100 times
bit_strings = []
n_samples = 200
for i in range(0, n_samples):
bit_strings.append(bitarray_Z_to_int(circuit(params[0], params[1], sample=True, n_layers=n_layers)))
# print optimal parameters and most frequently sampled bitstring
counts = np.bincount(np.array(bit_strings))
most_freq_bit_string = np.argmax(counts)
print("Optimized (gamma, beta) vectors:\n{}".format(params[:, :n_layers]))
formatstring = "Most frequently sampled bit string is: {:0" + str(n_wires) + "b}"
print(formatstring.format(most_freq_bit_string))
f.close()
return -objective(params), bit_strings, most_freq_bit_string
_, bitstrings1, most_freq_1 = qaoa_maxclique(n_layers=1)
_, bitstrings2, most_freq_2 = qaoa_maxclique(n_layers=2)
_, bitstrings3, most_freq_3 = qaoa_maxclique(n_layers=3)
_, bitstrings4, most_freq_4 = qaoa_maxclique(n_layers=4)
_, bitstrings5, most_freq_5 = qaoa_maxclique(n_layers=5)
_, bitstrings6, most_freq_6 = qaoa_maxclique(n_layers=6)
xticks = range(0, pow(2, n_wires))
print(xticks)
xtick_labels = list(map(lambda x: x if x % (pow(2, n_wires-2) - 1) == 0 else None, xticks))
print(xtick_labels)
bins = np.arange(0, pow(2, n_wires) + 1) - 0.5
print(bins)
fig, (ax1) = plt.subplots(2, 3, figsize=(15, 10))
plt.subplot(2, 3, 1)
format_title = "n_layers={}, max={:0" + str(n_wires) + "b}"
plt.title(format_title.format(1, most_freq_1))
plt.xlabel("bitstrings")
plt.ylabel("freq.")
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings1, bins=bins)
plt.subplot(2, 3, 2)
plt.title(format_title.format(2, most_freq_2))
plt.xlabel("bitstrings")
plt.ylabel("freq.")
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings2, bins=bins)
plt.subplot(2, 3, 3)
plt.title(format_title.format(3, most_freq_3))
plt.xlabel("bitstrings")
plt.ylabel("freq.")
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings3, bins=bins)
plt.subplot(2, 3, 4)
plt.title(format_title.format(4, most_freq_4))
plt.xlabel("bitstrings")
plt.ylabel("freq.")
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings4, bins=bins)
plt.subplot(2, 3, 5)
plt.title(format_title.format(5, most_freq_5))
plt.xlabel("bitstrings")
plt.ylabel("freq.")
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings5, bins=bins)
plt.subplot(2, 3, 6)
plt.title(format_title.format(6, most_freq_6))
plt.xlabel("bitstrings")
plt.ylabel("freq.")
plt.xticks(xticks, xtick_labels, rotation="vertical")
plt.hist(bitstrings6, bins=bins)
plt.tight_layout()
plt.savefig('/home/jockel/master_2/QAOA/results_graph_' + str(index) + '_seed_' + str(seed) + '.pdf')