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 decode_result(result, offset=0):
x = max_cut.sample_most_likely(result['eigvecs'][0])
print('energy:', result['energy'])
print('max-cut objective:', result['energy'] + offset)
print('solution:', max_cut.get_graph_solution(x))
print('solution objective:', max_cut.max_cut_value(x, w))
return " "
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)
pauli_list.append([value, Pauli(zp, xp)])
return WeightedPauliOperator(paulis=pauli_list), constant
isingdict ={(0,1):0.5,(1,2):0.5,(2,3):0.5,(3,4):0.5,():-2.0}
qubitOp, shift = get_ising_qubitops(isingdict,5)
provider = OpenSuperQ_Provider()
backend = provider.get_backend('OSQ_ETH7_rev1')
optimizer = POWELL()
qaoa = QAOA(qubitOp, optimizer, p=1, operator_mode='paulis',initial_point=[0.0, 0.0])
quantum_instance = QuantumInstance(backend)
circ = qaoa.construct_circuit(parameter=[2.0, 1.0],circuit_name_prefix='Hello',statevector_mode=True)
latex = circ[0].draw(output='latex_source')
result = qaoa.run(quantum_instance)
print('shift: ', shift)
print('name: ', result.keys())
print([str(key) + " " + str(value) for key, value in result.items()])
plot_histogram(result['eigvecs'], figsize = (18,5))
x = max_cut.sample_most_likely(result['eigvecs'][0])
graph_solution = max_cut.get_graph_solution(x)
#qaoa._circuit.draw(output='mpl')
print('Hello')
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)
def optimize_f(precision, coefs_param, beta):
coefs = coefs_param.copy()
coefs.append(0)
coefs_restr = (1, 1, 1)
lista_vars = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h']
mdl = Model()
variables = []
for num_var in range(len(coefs)):
tmp = {
i: mdl.binary_var(name=(lista_vars[num_var] + '_{0}').format(i))
for i in range(precision)
}
variables.append(tmp)
# x = {i: mdl.binary_var(name='x_{0}'.format(i)) for i in range(precision)}
# y = {i: mdl.binary_var(name='y_{0}'.format(i)) for i in range(precision)}
# z = {i: mdl.binary_var(name='z_{0}'.format(i)) for i in range(precision)}
#print(variables)
# Object function
# my_func = mdl.sum(coefs[0]*(2**i)*x[i]+coefs[1]*(2**i)*y[i]+(2**i)*coefs[2]*z[i] for i in range(precision))
# my_func = mdl.sum(coefs[j]*(2**i)*vars[j][i] for j in range(len(coefs)) for i in range(precision))
my_func = mdl.sum(coefs[j] * (2**i) * variables[j][i]
for j in range(len(coefs)) for i in range(precision))
# (x[i] for i in range(precision)), (y[i] for i in range(precision)), (z[i] for i in range(precision)
# tmp = {0:{'var':x,'coef':coefs_restr[0]},
# 1:{'var':y,'coef':coefs_restr[1]},
# 2:{'var':z,'coef':coefs_restr[2]}}
mdl.maximize(my_func)
inverted_beta = dameInversoBinario(beta, precision, len(coefs))
# mdl.add_constraint(mdl.sum( tmp[v]['var'][i]*(2**i)*tmp[v]['coef'] for v in range(len(coefs)) for i in range(precision)) == beta)
# mdl.add_constraint(mdl.sum( x[0] + x[1]*(2) + x[2]*(4) + y[0] + y[1]*(2) + y[2]*(4) + z[0] + z[1]*(2) + z[2]*(4) ) == inverted_beta)
# mdl.add_constraint(mdl.sum( variables[0][0] + variables[0][1]*(2) + variables[0][2]*(4) + variables[1][0] + variables[1][1]
# *(2) + variables[1][2]*(4) + variables[2][0] + variables[2][1]*(2) + variables[2][2]*(4) ) == inverted_beta)
mdl.add_constraint(
mdl.sum(variables[v][i] * 2**i for v in range(len(coefs))
for i in range(precision)) == inverted_beta)
# mdl.add_constraint(mdl.sum( -x[0] - x[1]*(2) - x[2]*(4) - y[0] - y[1]*(2) - y[2]*(4) - z[0] - z[1]*(2) - z[2]*(4) ) == 6)
# mdl.add_constraint(mdl.sum( -1*tmp[v]['var'][i]*(2**i)*tmp[v]['coef'] for v in range(len(coefs)) for i in range(precision)) == -beta)
qubitOp_docplex, offset_docplex = docplex.get_qubitops(mdl)
#print(qubitOp_docplex)
# algo_input = EnergyInput(qubitOp_docplex)
# print(algo_input.)
# ee = VQE(qubitOp_docplex)
# ee.run()
ee = ExactEigensolver(qubitOp_docplex, k=1)
result_ee = ee.run()
x_ee = max_cut.sample_most_likely(result_ee['eigvecs'][0])
print('solution:', max_cut.get_graph_solution(x_ee))
solucion_ee = max_cut.get_graph_solution(x_ee)
return (solucion_ee, None)
"""
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('max-cut objective:', result['energy'] + offset_docplex)
# print('solution:', max_cut.get_graph_solution(x))
# print('solution objective:', max_cut.max_cut_value(x, w))
seed = 10598
# change optimizer(spsa), change ry (riyc)
spsa = SPSA(max_trials=300)
ry = RY(qubitOp_docplex.num_qubits, depth=6, entanglement='linear')
vqe = VQE(qubitOp_docplex, ry, spsa, 'matrix')
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend,
seed=seed,
seed_transpiler=seed)
result = vqe.run(quantum_instance)
x = max_cut.sample_most_likely(result['eigvecs'][0])
print('solution:', max_cut.get_graph_solution(x))
return (solucion_ee, max_cut.get_graph_solution(x))
