def test_qaoa(self, w, prob, m, solutions):
""" QAOA test """
seed = 0
aqua_globals.random_seed = seed
self.log.debug('Testing %s-step QAOA with MaxCut on graph\n%s', prob,
w)
backend = BasicAer.get_backend('statevector_simulator')
optimizer = COBYLA()
qubit_op, offset = max_cut.get_operator(w)
qaoa = QAOA(qubit_op, optimizer, prob, mixer=m)
quantum_instance = QuantumInstance(backend,
seed_simulator=seed,
seed_transpiler=seed)
result = qaoa.run(quantum_instance)
x = sample_most_likely(result['eigvecs'][0])
graph_solution = max_cut.get_graph_solution(x)
self.log.debug('energy: %s', result['energy'])
self.log.debug('time: %s', result['eval_time'])
self.log.debug('maxcut objective: %s', result['energy'] + offset)
self.log.debug('solution: %s', graph_solution)
self.log.debug('solution objective: %s', max_cut.max_cut_value(x, w))
self.assertIn(''.join([str(int(i)) for i in graph_solution]),
solutions)
def test_set_packing_vqe(self):
""" set packing vqe test """
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
algorithm_cfg = {'name': 'VQE', 'max_evals_grouped': 2}
optimizer_cfg = {'name': 'SPSA', 'max_trials': 200}
var_form_cfg = {'name': 'RY', 'depth': 5, 'entanglement': 'linear'}
params = {
'problem': {
'name': 'ising',
'random_seed': 100
},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = Aer.get_backend('qasm_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = set_packing.get_solution(x)
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def test_tsp(self):
""" TSP test """
algo = NumPyMinimumEigensolver(self.qubit_op)
result = algo.run()
x = sample_most_likely(result.eigenstate)
order = tsp.get_tsp_solution(x)
np.testing.assert_array_equal(order, [1, 2, 0])
def test_tsp(self):
""" TSP test """
algo = ExactEigensolver(self.qubit_op, k=1)
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
order = tsp.get_tsp_solution(x)
np.testing.assert_array_equal(order, [1, 2, 0])
def test_vertex_cover_vqe(self):
""" Vertex Cover VQE test """
algorithm_cfg = {'name': 'VQE', 'max_evals_grouped': 2}
optimizer_cfg = {'name': 'SPSA', 'max_trials': 200}
var_form_cfg = {
'name': 'RYRZ',
'depth': 3,
}
params = {
'problem': {
'name': 'ising',
'random_seed': self.seed
},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('qasm_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
sol = vertex_cover.get_graph_solution(x)
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(sol), oracle)
def test_set_packing_vqe(self):
""" set packing vqe test """
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
aqua_globals.random_seed = 50
result = VQE(self.qubit_op,
RY(self.qubit_op.num_qubits,
depth=5,
entanglement='linear'),
SPSA(max_trials=200),
max_evals_grouped=2).run(
QuantumInstance(
Aer.get_backend('qasm_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
x = sample_most_likely(result['eigvecs'][0])
ising_sol = set_packing.get_solution(x)
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def test_exact_cover_vqe(self):
""" Exact Cover VQE test """
algorithm_cfg = {'name': 'VQE', 'max_evals_grouped': 2}
optimizer_cfg = {'name': 'COBYLA'}
var_form_cfg = {'name': 'RYRZ', 'depth': 5}
params = {
'problem': {
'name': 'ising',
'random_seed': 10598
},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('statevector_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = exact_cover.get_solution(x)
oracle = self._brute_force()
self.assertEqual(
exact_cover.check_solution_satisfiability(ising_sol,
self.list_of_subsets),
oracle)
def test_clique_vqe(self):
""" VQE Clique test """
algorithm_cfg = {'name': 'VQE', 'max_evals_grouped': 2}
optimizer_cfg = {'name': 'COBYLA'}
var_form_cfg = {'name': 'RY', 'depth': 5, 'entanglement': 'linear'}
params = {
'problem': {
'name': 'ising',
'random_seed': 10598
},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('statevector_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k),
oracle)
def test_graph_partition_vqe(self):
""" Graph Partition VQE test """
algorithm_cfg = {'name': 'VQE', 'max_evals_grouped': 2}
optimizer_cfg = {'name': 'SPSA', 'max_trials': 300}
var_form_cfg = {'name': 'RY', 'depth': 5, 'entanglement': 'linear'}
params = {
'problem': {
'name': 'ising',
'random_seed': 10598
},
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg
}
backend = BasicAer.get_backend('statevector_simulator')
result = run_algorithm(params, self.algo_input, backend=backend)
x = sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_partition_direct(self):
""" Partition Direct test """
algo = ExactEigensolver(self.algo_input.qubit_op,
k=1,
aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
np.testing.assert_array_equal(x, [0, 1, 0])
def test_vertex_cover(self):
""" Vertex Cover test """
algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
result = algo.run()
x = sample_most_likely(result.eigenstate)
sol = vertex_cover.get_graph_solution(x)
np.testing.assert_array_equal(sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(sol), oracle)
def test_portfolio(self):
""" portfolio test """
algo = ExactEigensolver(self.qubit_op)
result = algo.run()
selection = sample_most_likely(result['eigvecs'][0])
value = portfolio.portfolio_value(selection, self.muu, self.sigma,
self.risk, self.budget, self.penalty)
np.testing.assert_array_equal(selection, [1, 0, 0, 1])
self.assertAlmostEqual(value, -0.0055989)
def test_clique_direct(self):
""" Clique Direct test """
algo = ExactEigensolver(self.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k), oracle)
def test_set_packing(self):
""" set packing test """
algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
result = algo.run()
x = sample_most_likely(result.eigenstate)
ising_sol = set_packing.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def test_vertex_cover_direct(self):
""" Vertex Cover Direct test """
algo = ExactEigensolver(self.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
sol = vertex_cover.get_graph_solution(x)
np.testing.assert_array_equal(sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(sol), oracle)
def test_set_packing_direct(self):
""" set packing direct test """
algo = ExactEigensolver(self.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
ising_sol = set_packing.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def test_stable_set(self):
""" Stable set test """
algo = ExactEigensolver(self.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
self.assertAlmostEqual(result['energy'], -29.5)
self.assertAlmostEqual(result['energy'] + self.offset, -25.0)
ising_sol = stable_set.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 0, 1, 1, 1])
self.assertEqual(stable_set.stable_set_value(x, self.w), (3.0, False))
def test_graph_partition_direct(self):
""" Graph Partition Direct test """
algo = ExactEigensolver(self.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_graph_partition(self):
""" Graph Partition test """
algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
result = algo.run()
x = sample_most_likely(result.eigenstate)
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 0, 1])
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_clique(self):
""" Clique test """
algo = NumPyMinimumEigensolver(self.qubit_op, aux_operators=[])
result = algo.run()
x = sample_most_likely(result.eigenstate)
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k),
oracle)
def test_exact_cover_direct(self):
""" Exact Cover Direct test """
algo = ExactEigensolver(self.qubit_op, k=1, aux_operators=[])
result = algo.run()
x = sample_most_likely(result['eigvecs'][0])
ising_sol = exact_cover.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1, 0])
oracle = self._brute_force()
self.assertEqual(exact_cover.check_solution_satisfiability(ising_sol, self.list_of_subsets),
oracle)
def test_set_packing(self):
""" set packing test """
params = {
'problem': {'name': 'ising'},
'algorithm': {'name': 'ExactEigensolver'}
}
result = run_algorithm(params, self.algo_input)
x = sample_most_likely(result['eigvecs'][0])
ising_sol = set_packing.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def _run_knapsack(values, weights, max_weight):
qubit_op, _ = knapsack.get_operator(values, weights, max_weight)
algo = NumPyMinimumEigensolver(qubit_op)
result = algo.run()
x = sample_most_likely(result.eigenstate)
solution = knapsack.get_solution(x, values)
value, weight = knapsack.knapsack_value_weight(solution, values,
weights)
return solution, value, weight
def test_clique(self):
""" Clique test """
params = {
'problem': {'name': 'ising'},
'algorithm': {'name': 'ExactEigensolver'}
}
result = run_algorithm(params, EnergyInput(self.qubit_op))
x = sample_most_likely(result['eigvecs'][0])
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k), oracle)
def run_simulation(self, backend):
#
# Random 3-regular graph with 12 nodes
#
n = int(os.environ.get("N", "4"))
graph = nx.random_regular_graph(3, n)
for e in graph.edges():
graph[e[0]][e[1]]['weight'] = 1.0
# Compute the weight matrix from the graph
w = np.zeros([n, n])
for i in range(n):
for j in range(n):
temp = graph.get_edge_data(i, j, default=0)
if temp != 0:
w[i, j] = temp["weight"]
# Create an Ising Hamiltonian with docplex.
mdl = Model(name="max_cut")
mdl.node_vars = mdl.binary_var_list(list(range(n)), name="node")
maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] *
(1 - mdl.node_vars[j]) for i in range(n)
for j in range(n))
mdl.maximize(maxcut_func)
qubit_op, offset = docplex.get_operator(mdl)
# Run quantum algorithm QAOA on qasm simulator
seed = int(os.environ.get("SEED", "40598"))
aqua_globals.random_seed = seed
spsa = SPSA(max_trials=250)
qaoa = QAOA(qubit_op, spsa, p=5, max_evals_grouped=4)
quantum_instance = QuantumInstance(backend,
shots=1024,
seed_simulator=seed,
seed_transpiler=seed,
optimization_level=0)
result = qaoa.run(quantum_instance)
x = sample_most_likely(result["eigvecs"][0])
result["solution"] = max_cut.get_graph_solution(x)
result["solution_objective"] = max_cut.max_cut_value(x, w)
result["maxcut_objective"] = result["energy"] + offset
"""
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))
"""
return result
def test_vertex_cover(self):
""" Vertex cover test """
params = {
'problem': {'name': 'ising'},
'algorithm': {'name': 'ExactEigensolver'}
}
result = run_algorithm(params, EnergyInput(self.qubit_op))
x = sample_most_likely(result['eigvecs'][0])
sol = vertex_cover.get_graph_solution(x)
np.testing.assert_array_equal(sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(sol), oracle)
def test_exact_cover(self):
""" Exact Cover test """
params = {
'problem': {'name': 'ising'},
'algorithm': {'name': 'ExactEigensolver'}
}
result = run_algorithm(params, EnergyInput(self.qubit_op))
x = sample_most_likely(result['eigvecs'][0])
ising_sol = exact_cover.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1, 0])
oracle = self._brute_force()
self.assertEqual(exact_cover.check_solution_satisfiability(ising_sol, self.list_of_subsets),
oracle)
def test_portfolio_qaoa(self):
""" portfolio test with QAOA """
qaoa = QAOA(self.qubit_op, COBYLA(maxiter=500))
backend = BasicAer.get_backend('statevector_simulator')
quantum_instance = QuantumInstance(backend=backend,
seed_simulator=self.seed,
seed_transpiler=self.seed)
result = qaoa.run(quantum_instance)
selection = sample_most_likely(result['eigvecs'][0])
value = portfolio.portfolio_value(selection, self.muu, self.sigma,
self.risk, self.budget, self.penalty)
np.testing.assert_array_equal(selection, [1, 0, 0, 1])
self.assertAlmostEqual(value, -0.0055989)
def test_partition(self):
""" Partition test """
params = {
'problem': {
'name': 'ising'
},
'algorithm': {
'name': 'ExactEigensolver'
}
}
result = run_algorithm(params, EnergyInput(self.qubit_op))
x = sample_most_likely(result['eigvecs'][0])
np.testing.assert_array_equal(x, [0, 1, 0])
def test_clique_vqe(self):
""" VQE Clique test """
aqua_globals.random_seed = 10598
result = VQE(self.qubit_op,
RY(self.qubit_op.num_qubits, depth=5, entanglement='linear'),
COBYLA(),
max_evals_grouped=2).run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
x = sample_most_likely(result['eigvecs'][0])
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k), oracle)
