def test_qaoa_energy_vs_qiskit(test_problem):
G, gamma, beta = test_problem
print('no_zz', G.number_of_nodes(), G.number_of_edges(), len(gamma))
sim = QAOAQtreeSimulator(QtreeQAOAComposer)
with prof.timing('QTensor energy time'):
E = sim.energy_expectation(G, gamma=gamma, beta=beta)
assert E
gamma, beta = -np.array(gamma) * 2 * np.pi, np.array(beta) * np.pi
with prof.timing('Qiskit energy time'):
qiskit_E = simulate_qiskit_amps(G, gamma, beta)
assert np.isclose(E, qiskit_E)
def test_qaoa_energy_multithread():
G, gamma, beta = get_test_problem()
sim = QAOAQtreeSimulator(QtreeQAOAComposer)
res = sim.energy_expectation_parallel(G,
gamma=gamma,
beta=beta,
n_processes=4)
print('result parallel', res)
assert res
res_1 = sim.energy_expectation(G, gamma=gamma, beta=beta)
print('result serial', res_1)
assert res_1 - res < 1e-6
def test_default_qaoa_energy_vs_qiskit(test_problem):
G, gamma, beta = test_problem
print('default', G.number_of_nodes(), G.number_of_edges(), len(gamma))
sim = QAOAQtreeSimulator(DefaultQAOAComposer)
with prof.timing('QTensor energy time'):
E = sim.energy_expectation(G, gamma=gamma, beta=beta)
assert E
gamma, beta = -np.array(gamma) * 2 * np.pi, np.array(beta) * np.pi
with prof.timing('Qiskit energy time'):
qiskit_E = simulate_qiskit_amps(G, gamma, beta, shots=QISKIT_SHOTS)
assert np.isclose(E, qiskit_E, rtol=REL_MARGIN)
def qaoa_energy_sim(nodes,
seed,
degree,
p,
graph_type,
max_time,
max_tw,
ordering_algo,
backend,
n_processes,
profile,
composer_type='default'):
np.random.seed(seed)
random.seed(seed)
if graph_type == 'random_regular':
G = nx.random_regular_graph(degree, nodes, seed=seed)
elif graph_type == 'erdos_renyi':
G = nx.erdos_renyi_graph(nodes, degree / (nodes - 1), seed=seed)
else:
raise Exception('Unsupported graph type')
gamma, beta = [np.pi / 3] * p, [np.pi / 2] * p
optimizer = get_ordering_algo(ordering_algo)
Backend = choose_backend(backend)
backend_obj = Backend()
if profile:
backend_obj = PerfBackend(print=False)
backend_obj.backend = Backend()
sim = QAOAQtreeSimulator(DefaultQAOAComposer,
bucket_backend=backend_obj,
optimizer=optimizer)
start = time.time()
if n_processes == 1:
result = sim.energy_expectation(G, gamma, beta)
if profile:
print('Profiling results')
backend_obj.gen_report()
else:
result = sim.energy_expectation_parallel(G,
gamma,
beta,
n_processes=n_processes)
end = time.time()
print(f"Simutation time: {end - start}")
print(result)
def test_qtree_energy():
G, gamma, beta = get_test_problem(16, 2, d=3)
sim = QAOAQtreeSimulator(QtreeQAOAComposer)
E = sim.energy_expectation(G=G, gamma=gamma, beta=beta)
print('Energy', E)
assert np.imag(E) < 1e-6
E = np.real(E)
Ed = G.number_of_edges()
C = (Ed - E) / 2
print("Edges", Ed)
print("Cost", C)
assert E
def test_qaoa_energy_feynman():
G, gamma, beta = get_test_problem(10, 3, 3)
sim = QAOAQtreeSimulator(QtreeQAOAComposer)
res = sim.energy_expectation(G, gamma=gamma, beta=beta)
print('result simple simulator', res)
sim = FeynmanQAOASimulator(QtreeQAOAComposer)
sim.optimizer = TreeTrimSplitter(max_tw=13, tw_bias=0)
res_1 = sim.energy_expectation(G, gamma=gamma, beta=beta)
print('result feynman simulator', res_1)
assert np.isclose(res, res_1)
from qtensor.optimisation.Optimizer import OrderingOptimizer from qtensor.optimisation.TensorNet import QtreeTensorNet from qtensor import QtreeQAOAComposer from qtensor import QAOAQtreeSimulator from qtensor import PerfNumpyBackend from qtensor import QAOA_energy import networkx as nx import numpy as np G = nx.random_regular_graph(4, 20) gamma, beta = [np.pi / 3], [np.pi / 2] composer = QtreeQAOAComposer(graph=G, gamma=gamma, beta=beta) composer.ansatz_state() backend = PerfNumpyBackend(print=True) sim = QAOAQtreeSimulator(composer, bucket_backend=backend) sim.simulate(composer.circuit) print(sim.bucket_backend) print(backend._profile_results) print(backend.gen_report())