def test_qaoa(self, w, p, m, solutions):
self.log.debug('Testing {}-step QAOA with MaxCut on graph\n{}'.format(
p, w))
np.random.seed(0)
backend = BasicAer.get_backend('statevector_simulator')
optimizer = COBYLA()
qubitOp, offset = max_cut.get_max_cut_qubitops(w)
qaoa = QAOA(qubitOp, optimizer, p, operator_mode='matrix', mixer=m)
quantum_instance = QuantumInstance(backend)
result = qaoa.run(quantum_instance)
x = max_cut.sample_most_likely(result['eigvecs'][0])
graph_solution = max_cut.get_graph_solution(x)
self.log.debug('energy: {}'.format(result['energy']))
self.log.debug('time: {}'.format(result['eval_time']))
self.log.debug('maxcut objective: {}'.format(result['energy'] +
offset))
self.log.debug('solution: {}'.format(graph_solution))
self.log.debug('solution objective: {}'.format(
max_cut.max_cut_value(x, w)))
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)
if quantum_instance.has_circuit_caching:
self.assertLess(quantum_instance._circuit_cache.misses, 3)
def get_qubit_op(num_qubits, seed=23412341234):
G = nx.Graph()
# """ HAVE DATA -- VIGO
n = num_qubits
SEED = seed
np.random.seed(SEED % (2 ** 31 - 1))
G = nx.dense_gnm_random_graph(n, n ** 2, seed=SEED)
for (u, v, w) in G.edges(data=True):
w['weight'] = np.random.rand() * 0.5 # 0.1
np.random.seed(int(time.time()))
w = np.zeros([n, n])
for i in range(n):
for j in range(n):
temp = G.get_edge_data(i, j, default=0)
if temp != 0:
w[i, j] = temp['weight']
# print(w)
qubitOp, offset = max_cut.get_max_cut_qubitops(w)
algo_input = EnergyInput(qubitOp)
pos = nx.spring_layout(G)
exact = ExactEigensolver(qubitOp).run()
exact_energy = exact['energy']
return qubitOp, exact_energy
def test_qaoa(self, w, p, m, solutions):
os.environ.pop('QISKIT_AQUA_CIRCUIT_CACHE', None)
self.log.debug('Testing {}-step QAOA with MaxCut on graph\n{}'.format(
p, w))
np.random.seed(0)
backend = BasicAer.get_backend('statevector_simulator')
optimizer = COBYLA()
qubit_op, offset = max_cut.get_max_cut_qubitops(w)
qaoa = QAOA(qubit_op, optimizer, p, mixer=m)
# TODO: cache fails for QAOA since we construct the evolution circuit via instruction
quantum_instance = QuantumInstance(backend, circuit_caching=False)
result = qaoa.run(quantum_instance)
x = max_cut.sample_most_likely(result['eigvecs'][0])
graph_solution = max_cut.get_graph_solution(x)
self.log.debug('energy: {}'.format(result['energy']))
self.log.debug('time: {}'.format(result['eval_time']))
self.log.debug('maxcut objective: {}'.format(result['energy'] +
offset))
self.log.debug('solution: {}'.format(graph_solution))
self.log.debug('solution objective: {}'.format(
max_cut.max_cut_value(x, w)))
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)
if quantum_instance.has_circuit_caching:
self.assertLess(quantum_instance._circuit_cache.misses, 3)
def set_adj_matrix(self, adj_matrix):
maxcut_op, self.maxcut_shift = max_cut.get_max_cut_qubitops(adj_matrix)
# print("maxcut_op: ", maxcut_op, ", maxcut_shift: ", maxcut_shift)
# TODO: Find different approach of calculating and retrieving diagonal
maxcut_op._paulis_to_matrix()
self.eigenvalues = maxcut_op._dia_matrix
self.calc_expectation_value()
self.draw_expectation_grid()
def setUp(self):
super().setUp()
np.random.seed(8123179)
self.w = max_cut.random_graph(4, edge_prob=0.5, weight_range=10)
self.qubit_op, self.offset = max_cut.get_max_cut_qubitops(self.w)
self.algo_input = EnergyInput(self.qubit_op)
# tuple is (i,j,weight) where (i,j) is the edge
G.add_weighted_edges_from(elist)
print(list(nx.connected_components(G)))
print(list(G.edges))
square_ring = list(G.edges)
print(square_ring)
w = np.zeros([n, n])
for i in range(n):
for j in range(n):
temp = G.get_edge_data(i, j, default=0)
if temp != 0:
w[i, j] = temp['weight']
print(w)
qubitOp, offset = max_cut.get_max_cut_qubitops(w)
algo_input = EnergyInput(qubitOp)
seed = 10598
spsa = SPSA(max_trials=300)
ry = RY(qubitOp.num_qubits, depth=5, entanglement='linear')
vqe = VQE(qubitOp, ry, spsa, 'matrix')
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend, seed=seed, seed_transpiler=seed)
result = vqe.run(quantum_instance)
"""declarative approach
algorithm_cfg = {
def qiskit_maxcut():
n = 4 # Number of nodes in graph
G = nx.Graph()
G.add_nodes_from(np.arange(0, n, 1))
elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)]
# tuple is (i,j,weight) where (i,j) is the edge
G.add_weighted_edges_from(elist)
colors = ['r' for node in G.nodes()]
pos = nx.spring_layout(G)
default_axes = plt.axes(frameon=True)
nx.draw_networkx(G,
node_color=colors,
node_size=600,
alpha=.8,
ax=default_axes,
pos=pos)
w = np.zeros([n, n])
for i in range(n):
for j in range(n):
temp = G.get_edge_data(i, j, default=0)
if temp != 0:
w[i, j] = temp['weight']
# print(w) # weight matrix. i.e. distances. 0 indicates no path.
#####
# Brute force solution
# best_cost_brute = 0
# for b in range(2 ** n):
# x = [int(t) for t in reversed(list(bin(b)[2:].zfill(n)))]
# cost = 0
# for i in range(n):
# for j in range(n):
# cost = cost + w[i, j] * x[i] * (1 - x[j])
# if best_cost_brute < cost:
# best_cost_brute = cost
# xbest_brute = x
# print('case = ' + str(x) + ' cost = ' + str(cost))
#
# colors = ['r' if xbest_brute[i] == 0 else 'b' for i in range(n)]
# nx.draw_networkx(G, node_color=colors, node_size=600, alpha=.8, pos=pos)
# print('\nBest solution = ' + str(xbest_brute) + ' cost = ' + str(best_cost_brute))
# End brute force solution
#####
#####
# Eigensolver solution
# qubitOp, offset = max_cut.get_max_cut_qubitops(w)
# algo_input = EnergyInput(qubitOp)
#
# # Making the Hamiltonian in its full form and getting the lowest eigenvalue and eigenvector
# ee = ExactEigensolver(qubitOp, k=1)
# result = ee.run()
#
# """
# algorithm_cfg = {
# 'name': 'ExactEigensolver',
# }
#
# params = {
# 'problem': {'name': 'ising'},
# 'algorithm': algorithm_cfg
# }
# result = run_algorithm(params,algo_input)
# """
# x = max_cut.sample_most_likely(result['eigvecs'][0])
# print('energy:', result['energy'])
# print('maxcut objective:', result['energy'] + offset)
# print('solution:', max_cut.get_graph_solution(x))
# print('solution objective:', max_cut.max_cut_value(x, w))
#
# colors = ['r' if max_cut.get_graph_solution(x)[i] == 0 else 'b' for i in range(n)]
# nx.draw_networkx(G, node_color=colors, node_size=600, alpha=.8, pos=pos)
# End eigensolver solution
#####
# run quantum algorithm with shots
qubitOp, offset = max_cut.get_max_cut_qubitops(w)
algo_input = EnergyInput(qubitOp)
seed = 10598
spsa = SPSA(max_trials=300)
ry = RY(qubitOp.num_qubits, depth=5, entanglement='linear')
vqe = VQE(qubitOp, ry, spsa, 'grouped_paulis')
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend=backend, shots=1024, seed=seed)
result = vqe.run(quantum_instance)
"""declarative approach, update the param from the previous cell.
params['algorithm']['operator_mode'] = 'grouped_paulis'
params['backend']['name'] = 'qasm_simulator'
params['backend']['shots'] = 1024
result = run_algorithm(params, algo_input)
"""
x = max_cut.sample_most_likely(result['eigvecs'][0])
print('energy:', result['energy'])
print('time:', result['eval_time'])
print('maxcut objective:', result['energy'] + offset)
print('solution:', max_cut.get_graph_solution(x))
print('solution objective:', max_cut.max_cut_value(x, w))
plot_histogram(result['eigvecs'][0])
colors = [
'r' if max_cut.get_graph_solution(x)[i] == 0 else 'b' for i in range(n)
]
nx.draw_networkx(G, node_color=colors, node_size=600, alpha=.8, pos=pos)