def test_measurement_error_mitigation_with_vqe(self):
""" measurement error mitigation test with vqe """
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
from qiskit.providers.aer import noise
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 = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=167,
seed_transpiler=167,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter)
h2_hamiltonian = -1.052373245772859 * (I ^ I) \
+ 0.39793742484318045 * (I ^ Z) \
- 0.39793742484318045 * (Z ^ I) \
- 0.01128010425623538 * (Z ^ Z) \
+ 0.18093119978423156 * (X ^ X)
optimizer = SPSA(maxiter=200)
var_form = EfficientSU2(2, reps=1)
vqe = VQE(
var_form=var_form,
operator=h2_hamiltonian,
quantum_instance=quantum_instance,
optimizer=optimizer,
)
result = vqe.compute_minimum_eigenvalue()
self.assertAlmostEqual(result.eigenvalue.real, -1.86, places=2)
def test_hhl_non_hermitian(self):
""" hhl non hermitian test """
self.log.debug('Testing HHL with simple non-hermitian matrix')
matrix = [[1, 1], [2, 1]]
vector = [1, 0]
# run NumPyLSsolver
ref_result = NumPyLSsolver(matrix, vector).run()
ref_solution = ref_result['solution']
ref_normed = ref_solution / np.linalg.norm(ref_solution)
# run hhl
orig_size = len(vector)
matrix, vector, truncate_powerdim, truncate_hermitian = HHL.matrix_resize(
matrix, vector)
# Initialize eigenvalue finding module
eigs = TestHHL._create_eigs(matrix, 6, True)
num_q, num_a = eigs.get_register_sizes()
# Initialize initial state module
init_state = Custom(num_q, state_vector=vector)
# Initialize reciprocal rotation module
reci = LookupRotation(negative_evals=eigs._negative_evals,
evo_time=eigs._evo_time)
algo = HHL(matrix, vector, truncate_powerdim, truncate_hermitian, eigs,
init_state, reci, num_q, num_a, orig_size)
hhl_result = algo.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
hhl_solution = hhl_result['solution']
hhl_normed = hhl_solution / np.linalg.norm(hhl_solution)
# compare result
fidelity = state_fidelity(ref_normed, hhl_normed)
self.assertGreater(fidelity, 0.8)
self.log.debug('HHL solution vector: %s', hhl_solution)
self.log.debug('algebraic solution vector: %s', ref_solution)
self.log.debug('fidelity HHL to algebraic: %s', fidelity)
self.log.debug('probability of result: %s',
hhl_result["probability_result"])
def test_qsvm_binary(self):
""" QSVM Binary test """
ref_kernel_training = np.array([[1., 0.85366667, 0.12341667, 0.36408333],
[0.85366667, 1., 0.11141667, 0.45491667],
[0.12341667, 0.11141667, 1., 0.667],
[0.36408333, 0.45491667, 0.667, 1.]])
ref_kernel_testing = np.array([[0.14316667, 0.18208333, 0.4785, 0.14441667],
[0.33608333, 0.3765, 0.02316667, 0.15858333]])
# ref_alpha = np.array([0.36064489, 1.49204209, 0.0264953, 1.82619169])
ref_alpha = np.array([0.34903335, 1.48325498, 0.03074852, 1.80153981])
# ref_bias = np.array([-0.03380763])
ref_bias = np.array([-0.03059226])
ref_support_vectors = np.array([[2.95309709, 2.51327412], [3.14159265, 4.08407045],
[4.08407045, 2.26194671], [4.46106157, 2.38761042]])
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = 2
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2,
entangler_map=[[0, 1]])
svm = QSVM(feature_map, self.training_data, self.testing_data, None)
quantum_instance = QuantumInstance(backend,
shots=self.shots,
seed_simulator=self.random_seed,
seed_transpiler=self.random_seed)
try:
result = svm.run(quantum_instance)
np.testing.assert_array_almost_equal(
result['kernel_matrix_training'], ref_kernel_training, decimal=1)
np.testing.assert_array_almost_equal(
result['kernel_matrix_testing'], ref_kernel_testing, decimal=1)
self.assertEqual(len(result['svm']['support_vectors']), 4)
np.testing.assert_array_almost_equal(
result['svm']['support_vectors'], ref_support_vectors, decimal=4)
np.testing.assert_array_almost_equal(result['svm']['alphas'], ref_alpha, decimal=8)
np.testing.assert_array_almost_equal(result['svm']['bias'], ref_bias, decimal=8)
self.assertEqual(result['testing_accuracy'], 0.5)
except NameError as ex:
self.skipTest(str(ex))
def test_measurement_error_mitigation_with_dedicated_shots(self):
""" measurement error mitigation with dedicated shots test """
# pylint: disable=import-outside-toplevel
try:
from qiskit import Aer
from qiskit.providers.aer import noise
except ImportError as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
aqua_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=1679,
seed_transpiler=167,
shots=100,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter,
cals_matrix_refresh_period=0)
oracle = LogicalExpressionOracle('a & b & c')
grover = Grover(oracle)
_ = grover.run(quantum_instance)
cals_matrix_1, timestamp_1 = quantum_instance.cals_matrix(
qubit_index=[0, 1, 2])
quantum_instance.measurement_error_mitigation_shots = 1000
_ = grover.run(quantum_instance)
cals_matrix_2, timestamp_2 = quantum_instance.cals_matrix(
qubit_index=[0, 1, 2])
diff = cals_matrix_1 - cals_matrix_2
total_diff = np.sum(np.abs(diff))
self.assertGreater(total_diff, 0.0)
self.assertGreater(timestamp_2, timestamp_1)
def test_vqe_var_forms(self, depth, places):
""" VQE Var Forms test """
aqua_globals.random_seed = self.seed
result = VQE(
self.qubit_op,
RY(self.qubit_op.num_qubits,
depth=depth,
entanglement='sca',
entanglement_gate='crx',
skip_final_ry=True),
L_BFGS_B()).run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
shots=1,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.assertAlmostEqual(result.eigenvalue.real,
-1.85727503,
places=places)
def test_vqe(self, var_form_type):
""" VQE test """
var_form = RYRZ(self.qubit_op.num_qubits)
if var_form_type is QuantumCircuit:
params = ParameterVector('θ', var_form.num_parameters)
var_form = var_form.construct_circuit(params)
vqe = VQE(self.qubit_op, var_form, L_BFGS_B())
result = vqe.run(QuantumInstance(BasicAer.get_backend('statevector_simulator'),
basis_gates=['u1', 'u2', 'u3', 'cx', 'id'],
coupling_map=[[0, 1]],
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.assertAlmostEqual(result.eigenvalue.real, -1.85727503)
np.testing.assert_array_almost_equal(result.eigenvalue.real, -1.85727503, 5)
self.assertEqual(len(result.optimal_point), 16)
self.assertIsNotNone(result.cost_function_evals)
self.assertIsNotNone(result.optimizer_time)
def test_exact_cover_vqe(self):
""" Exact Cover VQE test """
aqua_globals.random_seed = 10598
result = VQE(self.qubit_op,
RYRZ(self.qubit_op.num_qubits, depth=5),
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 = exact_cover.get_solution(x)
oracle = self._brute_force()
self.assertEqual(
exact_cover.check_solution_satisfiability(ising_sol,
self.list_of_subsets),
oracle)
def factorize(self):
shor = Shor(self.factor)
device = least_busy(
self.__provider.backends(
filters=lambda x: x.configuration().n_qubits >= 3 and not x.
configuration().simulator and x.status().operational == True))
print("Running on current least busy device: ", device)
quantum_instance = QuantumInstance(device,
shots=1024,
skip_qobj_validation=False)
computation = shor.run(quantum_instance)
if len(computation['factors']) == 0:
print("Algorithm went wrong")
return None, None
result = computation['factors'][0]
return result[0], result[1]
def test_bernstein_vazirani(self, bv_input, mct_mode, optimization,
simulator):
nbits = int(math.log(len(bv_input), 2))
# compute the ground-truth classically
parameter = ""
for i in reversed(range(nbits)):
bit = bv_input[2**i]
parameter += bit
backend = BasicAer.get_backend(simulator)
oracle = TruthTableOracle(bv_input,
optimization=optimization,
mct_mode=mct_mode)
algorithm = BernsteinVazirani(oracle)
quantum_instance = QuantumInstance(backend)
result = algorithm.run(quantum_instance=quantum_instance)
# print(result['circuit'].draw(line_length=10000))
self.assertEqual(result['result'], parameter)
def call_grover(truth_map: str, num_vertices: int, shots=1024) -> dict:
"""Call the simulation for grover's algorithm with the truth map and time its execution
:param truth_map: The string bitmap
:param num_vertices: Number of vertices of the graph for documentation purposes
:return: the GroverResult item
"""
start = time()
oracle = TruthTableOracle(truth_map)
grover = Grover(oracle) # Wow that's nice that this already exists
result = grover.run(
QuantumInstance(BasicAer.get_backend('qasm_simulator'), shots=shots))
end = time()
print('Grover\'s search on n = {} vertices:\nTime elapsed: {}s\n'.format(
num_vertices, end - start))
return result
def setUp(self):
super().setUp()
self._statevector = QuantumInstance(
backend=BasicAer.get_backend('statevector_simulator'),
circuit_caching=False,
seed_simulator=2,
seed_transpiler=2)
def qasm(shots=100):
return QuantumInstance(
backend=BasicAer.get_backend('qasm_simulator'),
shots=shots,
circuit_caching=False,
seed_simulator=2,
seed_transpiler=2)
self._qasm = qasm
def test_w_backend_options(self):
""" with backend options test """
# run with backend_options
quantum_instance = QuantumInstance(
self.backend,
seed_transpiler=self.random_seed,
seed_simulator=self.random_seed,
shots=1024,
backend_options={'initial_statevector': [.5, .5, .5, .5]})
res_w_bo = quantum_instance.execute(self.qc).get_counts(self.qc)
self.assertGreater(quantum_instance.time_taken, 0.)
quantum_instance.reset_execution_results()
quantum_instance.skip_qobj_validation = True
res_w_bo_skip_validation = quantum_instance.execute(
self.qc).get_counts(self.qc)
self.assertGreater(quantum_instance.time_taken, 0.)
quantum_instance.reset_execution_results()
self.assertTrue(_compare_dict(res_w_bo, res_w_bo_skip_validation))
def test_uccsd_hf_aer_qasm_snapshot(self):
""" uccsd hf test with Aer qasm_simulator snapshot. """
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
backend = Aer.get_backend('qasm_simulator')
optimizer = SPSA(maxiter=200, last_avg=5)
solver = VQE(var_form=self.var_form, optimizer=optimizer,
expectation=AerPauliExpectation(),
quantum_instance=QuantumInstance(backend=backend))
gsc = GroundStateEigensolver(self.fermionic_transformation, solver)
result = gsc.solve(self.driver)
self.assertAlmostEqual(result.total_energies[0], self.reference_energy, places=3)
def test_uccsd_hf_aer_qasm_snapshot(self):
""" uccsd hf test with Aer qasm_simulator snapshot. """
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
backend = Aer.get_backend('qasm_simulator')
algo = VQE(self.qubit_op,
self.var_form,
self.optimizer,
expectation=AerPauliExpectation())
result = algo.run(QuantumInstance(backend))
result = self.core.process_algorithm_result(result)
self.assertAlmostEqual(result.energy, self.reference_energy, places=6)
def test_vqe_callback(self):
tmp_filename = 'vqe_callback_test.csv'
is_file_exist = os.path.exists(self._get_resource_path(tmp_filename))
if is_file_exist:
os.remove(self._get_resource_path(tmp_filename))
def store_intermediate_result(eval_count, parameters, mean, std):
with open(self._get_resource_path(tmp_filename), 'a') as f:
content = "{},{},{:.5f},{:.5f}".format(eval_count, parameters, mean, std)
print(content, file=f, flush=True)
backend = BasicAer.get_backend('qasm_simulator')
num_qubits = self.algo_input.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 1, initial_state=init_state)
optimizer = COBYLA(maxiter=3)
algo = VQE(self.algo_input.qubit_op, var_form, optimizer, 'paulis',
callback=store_intermediate_result)
aqua_globals.random_seed = 50
quantum_instance = QuantumInstance(backend, seed_transpiler=50, shots=1024, seed_simulator=50)
algo.run(quantum_instance)
is_file_exist = os.path.exists(self._get_resource_path(tmp_filename))
self.assertTrue(is_file_exist, "Does not store content successfully.")
# check the content
ref_content = [['1', '[-0.03391886 -1.70850424 -1.53640265 -0.65137839]', '-0.61121', '0.01572'],
['2', '[ 0.96608114 -1.70850424 -1.53640265 -0.65137839]', '-0.79235', '0.01722'],
['3', '[ 0.96608114 -0.70850424 -1.53640265 -0.65137839]', '-0.82829', '0.01529']
]
try:
with open(self._get_resource_path(tmp_filename)) as f:
idx = 0
for record in f.readlines():
eval_count, parameters, mean, std = record.split(",")
self.assertEqual(eval_count.strip(), ref_content[idx][0])
self.assertEqual(parameters, ref_content[idx][1])
self.assertEqual(mean.strip(), ref_content[idx][2])
self.assertEqual(std.strip(), ref_content[idx][3])
idx += 1
finally:
if is_file_exist:
os.remove(self._get_resource_path(tmp_filename))
def test_application(self):
"""Test an end-to-end application."""
num_qubits = 3
# parameters for considered random distribution
s_p = 2.0 # initial spot price
vol = 0.4 # volatility of 40%
r = 0.05 # annual interest rate of 4%
t_m = 40 / 365 # 40 days to maturity
# resulting parameters for log-normal distribution
mu = ((r - 0.5 * vol ** 2) * t_m + np.log(s_p))
sigma = vol * np.sqrt(t_m)
mean = np.exp(mu + sigma ** 2 / 2)
variance = (np.exp(sigma ** 2) - 1) * np.exp(2 * mu + sigma ** 2)
stddev = np.sqrt(variance)
# lowest and highest value considered for the spot price;
# in between, an equidistant discretization is considered.
low = np.maximum(0, mean - 3 * stddev)
high = mean + 3 * stddev
bounds = (low, high)
# construct circuit factory for uncertainty model
uncertainty_model = LogNormalDistribution(num_qubits,
mu=mu, sigma=sigma ** 2, bounds=bounds)
# set the strike price (should be within the low and the high value of the uncertainty)
strike_price = 1.896
# create amplitude function
european_call_delta = EuropeanCallDelta(num_qubits, strike_price, bounds)
# create state preparation
state_preparation = european_call_delta.compose(uncertainty_model, front=True)
# run amplitude estimation
iae = IterativeAmplitudeEstimation(0.01, 0.05, state_preparation=state_preparation,
objective_qubits=[num_qubits])
backend = QuantumInstance(Aer.get_backend('qasm_simulator'),
seed_simulator=125, seed_transpiler=80)
result = iae.run(backend)
self.assertAlmostEqual(result.estimation, 0.8088790606143996)
def test_qpe(self, qubit_op, simulator, num_time_slices, n_ancillae):
self.algorithm = 'QPE'
self.log.debug('Testing QPE')
self.qubit_op = qubit_op
exact_eigensolver = ExactEigensolver(self.qubit_op, k=1)
results = exact_eigensolver.run()
self.ref_eigenval = results['eigvals'][0]
self.ref_eigenvec = results['eigvecs'][0]
self.log.debug('The exact eigenvalue is: {}'.format(self.ref_eigenval))
self.log.debug('The corresponding eigenvector: {}'.format(self.ref_eigenvec))
state_in = Custom(self.qubit_op.num_qubits, state_vector=self.ref_eigenvec)
iqft = Standard(n_ancillae)
qpe = QPE(self.qubit_op, state_in, iqft, num_time_slices, n_ancillae,
expansion_mode='suzuki', expansion_order=2,
shallow_circuit_concat=True)
backend = BasicAer.get_backend(simulator)
quantum_instance = QuantumInstance(backend, shots=100)
# run qpe
result = qpe.run(quantum_instance)
# report result
self.log.debug('top result str label: {}'.format(result['top_measurement_label']))
self.log.debug('top result in decimal: {}'.format(result['top_measurement_decimal']))
self.log.debug('stretch: {}'.format(result['stretch']))
self.log.debug('translation: {}'.format(result['translation']))
self.log.debug('final eigenvalue from QPE: {}'.format(result['energy']))
self.log.debug('reference eigenvalue: {}'.format(self.ref_eigenval))
self.log.debug('ref eigenvalue (transformed): {}'.format(
(self.ref_eigenval + result['translation']) * result['stretch'])
)
self.log.debug('reference binary str label: {}'.format(decimal_to_binary(
(self.ref_eigenval.real + result['translation']) * result['stretch'],
max_num_digits=n_ancillae + 3,
fractional_part_only=True
)))
np.testing.assert_approx_equal(result['energy'], self.ref_eigenval.real, significant=2)
def test_uvcc_vscf(self):
""" uvcc vscf test """
co2_2modes_2modals_2body = [[[[[0, 0, 0]], 320.8467332810141],
[[[0, 1, 1]], 1760.878530705873],
[[[1, 0, 0]], 342.8218290247543],
[[[1, 1, 1]], 1032.396323618631]],
[[[[0, 0, 0], [1, 0, 0]], -57.34003649795117],
[[[0, 0, 1], [1, 0, 0]], -56.33205925807966],
[[[0, 1, 0], [1, 0, 0]], -56.33205925807966],
[[[0, 1, 1], [1, 0, 0]], -60.13032761856809],
[[[0, 0, 0], [1, 0, 1]], -65.09576309934431],
[[[0, 0, 1], [1, 0, 1]], -62.2363839133389],
[[[0, 1, 0], [1, 0, 1]], -62.2363839133389],
[[[0, 1, 1], [1, 0, 1]], -121.5533969109279],
[[[0, 0, 0], [1, 1, 0]], -65.09576309934431],
[[[0, 0, 1], [1, 1, 0]], -62.2363839133389],
[[[0, 1, 0], [1, 1, 0]], -62.2363839133389],
[[[0, 1, 1], [1, 1, 0]], -121.5533969109279],
[[[0, 0, 0], [1, 1, 1]], -170.744837386338],
[[[0, 0, 1], [1, 1, 1]], -167.7433236025723],
[[[0, 1, 0], [1, 1, 1]], -167.7433236025723],
[[[0, 1, 1], [1, 1, 1]], -179.0536532281924]]]
basis = [2, 2]
bosonic_op = BosonicOperator(co2_2modes_2modals_2body, basis)
qubit_op = bosonic_op.mapping('direct', threshold=1e-5)
init_state = VSCF(basis)
num_qubits = sum(basis)
uvcc_varform = UVCC(num_qubits, basis, [0, 1], initial_state=init_state)
q_instance = QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_transpiler=90, seed_simulator=12)
optimizer = COBYLA(maxiter=1000)
algo = VQE(qubit_op, uvcc_varform, optimizer)
vqe_result = algo.run(q_instance)
energy = vqe_result['optimal_value']
self.assertAlmostEqual(energy, self.reference_energy, places=4)
def test_measurement_error_mitigation_with_diff_qubit_order(self):
""" measurement error mitigation with dedicated shots test """
# pylint: disable=import-outside-toplevel
from qiskit import Aer
from qiskit.providers.aer import noise
aqua_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1], [0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(backend=backend,
seed_simulator=1679,
seed_transpiler=167,
shots=1000,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter,
cals_matrix_refresh_period=0)
# circuit
qc1 = QuantumCircuit(2, 2)
qc1.h(0)
qc1.cx(0, 1)
qc1.measure(0, 0)
qc1.measure(1, 1)
qc2 = QuantumCircuit(2, 2)
qc2.h(0)
qc2.cx(0, 1)
qc2.measure(1, 0)
qc2.measure(0, 1)
# this should run smoothly
quantum_instance.execute([qc1, qc2])
# failure case
qc3 = QuantumCircuit(3, 3)
qc3.h(2)
qc3.cx(1, 2)
qc3.measure(2, 1)
qc3.measure(1, 2)
self.assertRaises(AquaError, quantum_instance.execute, [qc1, qc3])
def test_vqc_minibatching_no_gradient_support(self, use_circuits):
""" vqc minibatching with no gradient support test """
n_dim = 2 # dimension of each data point
seed = 1024
aqua_globals.random_seed = seed
_, training_input, test_input, _ = ad_hoc_data(training_size=6,
test_size=3,
n=n_dim,
gap=0.3,
plot_data=False)
backend = BasicAer.get_backend('statevector_simulator')
num_qubits = n_dim
optimizer = COBYLA(maxiter=40)
feature_map = SecondOrderExpansion(feature_dimension=num_qubits,
depth=2)
var_form = RYRZ(num_qubits=num_qubits, depth=3)
# convert to circuit if circuits should be used
if use_circuits:
x = ParameterVector('x', feature_map.feature_dimension)
feature_map = feature_map.construct_circuit(x)
theta = ParameterVector('theta', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
# set up algorithm
vqc = VQC(optimizer,
feature_map,
var_form,
training_input,
test_input,
minibatch_size=2)
# sort parameters for reproducibility
if use_circuits:
vqc._feature_map_params = list(x)
vqc._var_form_params = list(theta)
quantum_instance = QuantumInstance(backend,
seed_simulator=seed,
seed_transpiler=seed,
optimization_level=0)
result = vqc.run(quantum_instance)
self.log.debug(result['testing_accuracy'])
self.assertGreaterEqual(result['testing_accuracy'], 0.5)
def test_readme_sample(self):
""" readme sample test """
# pylint: disable=import-outside-toplevel,redefined-builtin
def print(*args):
""" overloads print to log values """
if args:
self.log.debug(args[0], *args[1:])
# --- Exact copy of sample code ----------------------------------------
from qiskit import BasicAer
from qiskit.aqua import QuantumInstance, aqua_globals
from qiskit.aqua.algorithms import VQC
from qiskit.aqua.components.optimizers import COBYLA
from qiskit.aqua.components.feature_maps import RawFeatureVector
from qiskit.ml.datasets import wine
from qiskit.circuit.library import TwoLocal
seed = 1376
aqua_globals.random_seed = seed
# Use Wine data set for training and test data
feature_dim = 4 # dimension of each data point
_, training_input, test_input, _ = wine(training_size=12,
test_size=4,
n=feature_dim)
feature_map = RawFeatureVector(feature_dimension=feature_dim)
vqc = VQC(COBYLA(maxiter=100), feature_map,
TwoLocal(feature_map.num_qubits, ['ry', 'rz'], 'cz', reps=3),
training_input, test_input)
result = vqc.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
shots=1024,
seed_simulator=seed,
seed_transpiler=seed))
print('Testing accuracy: {:0.2f}'.format(result['testing_accuracy']))
# ----------------------------------------------------------------------
self.assertGreater(result['testing_accuracy'], 0.8)
def test_hhl_random_hermitian(self):
""" hhl random hermitian test """
self.log.debug('Testing HHL with random hermitian matrix')
n = 2
matrix = rmg.random_hermitian(n, eigrange=[0, 1])
vector = aqua_globals.random.random(n)
# run NumPyLSsolver
ref_result = NumPyLSsolver(matrix, vector).run()
ref_solution = ref_result['solution']
ref_normed = ref_solution / np.linalg.norm(ref_solution)
# run hhl
orig_size = len(vector)
matrix, vector, truncate_powerdim, truncate_hermitian = HHL.matrix_resize(
matrix, vector)
# Initialize eigenvalue finding module
eigs = TestHHL._create_eigs(matrix, 4, False)
num_q, num_a = eigs.get_register_sizes()
# Initialize initial state module
init_state = Custom(num_q, state_vector=vector)
# Initialize reciprocal rotation module
reci = LookupRotation(negative_evals=eigs._negative_evals,
evo_time=eigs._evo_time)
algo = HHL(matrix, vector, truncate_powerdim, truncate_hermitian, eigs,
init_state, reci, num_q, num_a, orig_size)
warnings.filterwarnings('ignore', category=DeprecationWarning)
hhl_result = algo.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
warnings.filterwarnings('always', category=DeprecationWarning)
hhl_solution = hhl_result['solution']
hhl_normed = hhl_solution / np.linalg.norm(hhl_solution)
# compare result
fidelity = state_fidelity(ref_normed, hhl_normed)
np.testing.assert_approx_equal(fidelity, 1, significant=1)
def test_vqc_with_max_evals_grouped(self, mode):
""" vqc with max evals grouped test """
aqua_globals.random_seed = self.seed
optimizer = SPSA(max_trials=10,
save_steps=1,
c0=4.0,
c1=0.1,
c2=0.602,
c3=0.101,
c4=0.0,
skip_calibration=True)
data_preparation = self.data_preparation[mode]
wavefunction = self.ryrz_wavefunction[mode]
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
# set up algorithm
vqc = VQC(optimizer,
data_preparation,
wavefunction,
self.training_data,
self.testing_data,
max_evals_grouped=2)
if mode == 'wrapped':
warnings.filterwarnings('always', category=DeprecationWarning)
quantum_instance = QuantumInstance(
BasicAer.get_backend('qasm_simulator'),
shots=1024,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = vqc.run(quantum_instance)
with self.subTest('opt params'):
np.testing.assert_array_almost_equal(result['opt_params'],
self.ref_opt_params[mode],
decimal=8)
with self.subTest('training loss'):
np.testing.assert_array_almost_equal(result['training_loss'],
self.ref_train_loss[mode],
decimal=8)
with self.subTest('accuracy'):
self.assertEqual(1.0 if mode == 'wrapped' else 0.5,
result['testing_accuracy'])
def test_vqc_minibatching_with_gradient_support(self, mode):
""" vqc minibatching with gradient support test """
n_dim = 2 # dimension of each data point
seed = 1024
aqua_globals.random_seed = seed
_, training_input, test_input, _ = ad_hoc_data(training_size=4,
test_size=2,
n=n_dim,
gap=0.3,
plot_data=False)
backend = BasicAer.get_backend('statevector_simulator')
optimizer = L_BFGS_B(maxfun=30)
# set up data encoding circuit
data_preparation = self.data_preparation[mode]
# set up wavefunction
if mode == 'wrapped':
warnings.filterwarnings('ignore', category=DeprecationWarning)
wavefunction = RYRZ(2, depth=1)
else:
wavefunction = TwoLocal(2, ['ry', 'rz'],
'cz',
reps=1,
insert_barriers=True)
if mode == 'circuit':
wavefunction = QuantumCircuit(2).compose(wavefunction)
# set up algorithm
vqc = VQC(optimizer,
data_preparation,
wavefunction,
training_input,
test_input,
minibatch_size=2)
if mode == 'wrapped':
warnings.filterwarnings('always', category=DeprecationWarning)
quantum_instance = QuantumInstance(backend,
seed_simulator=seed,
seed_transpiler=seed)
result = vqc.run(quantum_instance)
self.assertGreater(result['testing_accuracy'], 0.2)
def run(self, quantum_instance=None, **kwargs):
"""Execute the algorithm with selected backend.
Args:
quantum_instance (QuantumInstance or BaseBackend): the experiemental setting.
Returns:
dict: results of an algorithm.
"""
if not self.configuration.get('classical', False):
if quantum_instance is None:
AquaError(
"Quantum device or backend is needed since you are running quantum algorithm."
)
if isinstance(quantum_instance, BaseBackend):
quantum_instance = QuantumInstance(quantum_instance)
quantum_instance.set_config(**kwargs)
self._quantum_instance = quantum_instance
return self._run()
def test_gradient_wrapper(self, backend):
"""Test the gradient wrapper for probability gradients
dp0/da = cos(a)sin(b) / 2
dp1/da = - cos(a)sin(b) / 2
dp0/db = sin(a)cos(b) / 2
dp1/db = - sin(a)cos(b) / 2
"""
method = 'param_shift'
a = Parameter('a')
b = Parameter('b')
params = [a, b]
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(params[0], q[0])
qc.rx(params[1], q[0])
op = CircuitStateFn(primitive=qc, coeff=1.)
shots = 8000
backend = BasicAer.get_backend(backend)
q_instance = QuantumInstance(backend=backend, shots=shots)
if method == 'fin_diff':
np.random.seed(8)
prob_grad = Gradient(grad_method=method,
epsilon=shots**(-1 / 6.)).gradient_wrapper(
operator=op,
bind_params=params,
backend=q_instance)
else:
prob_grad = Gradient(grad_method=method).gradient_wrapper(
operator=op, bind_params=params, backend=q_instance)
values = [[np.pi / 4, 0], [np.pi / 4, np.pi / 4], [np.pi / 2, np.pi]]
correct_values = [[[0, 0],
[1 / (2 * np.sqrt(2)), -1 / (2 * np.sqrt(2))]],
[[1 / 4, -1 / 4], [1 / 4, -1 / 4]],
[[0, 0], [-1 / 2, 1 / 2]]]
for i, value in enumerate(values):
result = prob_grad(value)
np.testing.assert_array_almost_equal(result,
correct_values[i],
decimal=1)
def test_vqe_qasm_snapshot_mode(self):
""" VQE Aer qasm_simulator snapshot mode 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
backend = Aer.get_backend('qasm_simulator')
num_qubits = self.qubit_op.num_qubits
init_state = Zero(num_qubits)
var_form = RY(num_qubits, 3, initial_state=init_state)
optimizer = L_BFGS_B()
algo = VQE(self.qubit_op, var_form, optimizer, max_evals_grouped=1)
quantum_instance = QuantumInstance(backend, shots=1,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = algo.run(quantum_instance)
self.assertAlmostEqual(result.eigenvalue.real, -1.85727503, places=6)
def test_circuit_sampler2(self, method):
"""Test the probability gradient with the circuit sampler
dp0/da = cos(a)sin(b) / 2
dp1/da = - cos(a)sin(b) / 2
dp0/db = sin(a)cos(b) / 2
dp1/db = - sin(a)cos(b) / 2
"""
a = Parameter('a')
b = Parameter('b')
params = [a, b]
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(params[0], q[0])
qc.rx(params[1], q[0])
op = CircuitStateFn(primitive=qc, coeff=1.)
shots = 8000
if method == 'fin_diff':
np.random.seed(8)
prob_grad = Gradient(grad_method=method, epsilon=shots ** (-1 / 6.)).convert(
operator=op,
params=params)
else:
prob_grad = Gradient(grad_method=method).convert(operator=op, params=params)
values_dict = [{a: [np.pi / 4], b: [0]}, {params[0]: [np.pi / 4], params[1]: [np.pi / 4]},
{params[0]: [np.pi / 2], params[1]: [np.pi]}]
correct_values = [[[0, 0], [1 / (2 * np.sqrt(2)), - 1 / (2 * np.sqrt(2))]],
[[1 / 4, -1 / 4], [1 / 4, - 1 / 4]],
[[0, 0], [- 1 / 2, 1 / 2]]]
backend = BasicAer.get_backend('qasm_simulator')
q_instance = QuantumInstance(backend=backend, shots=shots)
for i, value_dict in enumerate(values_dict):
sampler = CircuitSampler(backend=q_instance).convert(prob_grad,
params=value_dict)
result = sampler.eval()
np.testing.assert_array_almost_equal(result[0], correct_values[i], decimal=1)
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.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_vqe(self):
""" VQE test """
result = VQE(self.qubit_op,
RYRZ(self.qubit_op.num_qubits),
L_BFGS_B()).run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
basis_gates=['u1', 'u2', 'u3', 'cx', 'id'],
coupling_map=[[0, 1]],
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
self.assertAlmostEqual(result.eigenvalue.real, -1.85727503)
np.testing.assert_array_almost_equal(result.eigenvalue.real, -1.85727503, 5)
ref_opt_params = [-0.58294401, -1.86141794, -1.97209632, -0.54796022,
-0.46945572, 2.60114794, -1.15637845, 1.40498879,
1.14479635, -0.48416694, -0.66608349, -1.1367579,
-2.67097002, 3.10214631, 3.10000313, 0.37235089]
np.testing.assert_array_almost_equal(result.optimal_point, ref_opt_params, 5)
self.assertIsNotNone(result.cost_function_evals)
self.assertIsNotNone(result.optimizer_time)
