def test_cplex_ising_direct(self):
""" cplex ising direct test """
try:
algo = CPLEX_Ising(self.algo_input.qubit_op, display=0)
result = algo.run()
self.assertEqual(result['energy'], -20.5)
x_dict = result['x_sol']
x = np.array([x_dict[i] for i in sorted(x_dict.keys())])
np.testing.assert_array_equal(max_cut.get_graph_solution(x),
[1, 0, 1, 1])
self.assertEqual(max_cut.max_cut_value(x, self.w), 24)
except AquaError as ex:
self.skipTest(str(ex))
def test_cplex_ising_via_run_algorithm(self):
try:
params = {
'problem': {
'name': 'ising'
},
'algorithm': {
'name': 'CPLEX.Ising',
'display': 0
}
}
result = run_algorithm(params, self.algo_input)
self.assertEqual(result['energy'], -20.5)
x_dict = result['x_sol']
x = np.array([x_dict[i] for i in sorted(x_dict.keys())])
np.testing.assert_array_equal(max_cut.get_graph_solution(x),
[1, 0, 1, 1])
self.assertEqual(max_cut.max_cut_value(x, self.w), 24)
except AquaError as e:
self.skipTest(str(e))
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)
seed_transpiler=seed)
result = vqe.run(quantum_instance)
"""declarative approach, update the param from the previous cell.
params['backend']['provider'] = 'qiskit.BasicAer'
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('max-cut 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)
]
pos = nx.spring_layout(G)
nx.draw(G,
pos,
with_labels=True,
node_color=colors,
edge_color='gray',
width=7,
node_size=500,
font_size=8,
font_color='black')