def init_params(self, params, algo_input):
"""
Initialize via parameters dictionary and algorithm input instance
Args:
params: parameters dictionary
algo_input: EnergyInput instance
"""
if algo_input is None:
raise AlgorithmError("EnergyInput instance is required.")
operator = algo_input.qubit_op
qpe_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
num_time_slices = qpe_params.get(QPE.PROP_NUM_TIME_SLICES)
paulis_grouping = qpe_params.get(QPE.PROP_PAULIS_GROUPING)
expansion_mode = qpe_params.get(QPE.PROP_EXPANSION_MODE)
expansion_order = qpe_params.get(QPE.PROP_EXPANSION_ORDER)
num_ancillae = qpe_params.get(QPE.PROP_NUM_ANCILLAE)
# Set up initial state, we need to add computed num qubits to params
init_state_params = params.get(QuantumAlgorithm.SECTION_KEY_INITIAL_STATE)
init_state_params['num_qubits'] = operator.num_qubits
init_state = get_initial_state_instance(init_state_params['name'])
init_state.init_params(init_state_params)
# Set up iqft, we need to add num qubits to params which is our num_ancillae bits here
iqft_params = params.get(QuantumAlgorithm.SECTION_KEY_IQFT)
iqft_params['num_qubits'] = num_ancillae
iqft = get_iqft_instance(iqft_params['name'])
iqft.init_params(iqft_params)
self.init_args(
operator, init_state, iqft, num_time_slices, num_ancillae,
paulis_grouping=paulis_grouping, expansion_mode=expansion_mode,
expansion_order=expansion_order)
def test_evolution(self):
SIZE = 2
temp = np.random.random((2**SIZE, 2**SIZE))
h1 = temp + temp.T
qubitOp = Operator(matrix=h1)
temp = np.random.random((2**SIZE, 2**SIZE))
h1 = temp + temp.T
evoOp = Operator(matrix=h1)
state_in = get_initial_state_instance('CUSTOM')
state_in.init_args(SIZE, state='random')
evo_time = 1
num_time_slices = 100
dynamics = get_algorithm_instance('EOH')
dynamics.setup_quantum_backend(skip_transpiler=True)
# self.log.debug('state_out:\n\n')
dynamics.init_args(qubitOp, 'paulis', state_in, evoOp, evo_time,
num_time_slices)
ret = dynamics.run()
self.log.debug('Evaluation result: {}'.format(ret))
def configurar_hartreefock(operadorqubit, configuracionaqua,
propiedadesmolecula):
"""Esta función obtiene una instancia configurada del método de aproximación de la función de onda Hartree-Fock"""
HF = get_initial_state_instance('HartreeFock')
HF.init_args(
operadorqubit.num_qubits,
propiedadesmolecula["numero_de_orbitales_de_spin"],
configuracionaqua["general"]["tipo_de_mapeo"],
__necesaria_reduccion(configuracionaqua["general"]["tipo_de_mapeo"]),
propiedadesmolecula["numero_de_particulas"])
return HF
def test_vqe_direct(self):
num_qbits = self.algo_input.qubit_op.num_qubits
init_state = get_initial_state_instance('ZERO')
init_state.init_args(num_qbits)
var_form = get_variational_form_instance('RY')
var_form.init_args(num_qbits, 3, initial_state=init_state)
optimizer = get_optimizer_instance('L_BFGS_B')
optimizer.init_args()
algo = get_algorithm_instance('VQE')
algo.setup_quantum_backend(backend='local_statevector_simulator')
algo.init_args(self.algo_input.qubit_op, 'matrix', var_form, optimizer)
result = algo.run()
self.assertAlmostEqual(result['energy'], -1.85727503)
def init_params(self, params, algo_input):
"""
Initialize via parameters dictionary and algorithm input instance
Args:
params (dict): parameters dictionary
algo_input (EnergyInput): EnergyInput instance
"""
if algo_input is None:
raise AlgorithmError("EnergyInput instance is required.")
operator = algo_input.qubit_op
vqe_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
operator_mode = vqe_params.get('operator_mode')
initial_point = vqe_params.get('initial_point')
# Set up initial state, we need to add computed num qubits to params
init_state_params = params.get(
QuantumAlgorithm.SECTION_KEY_INITIAL_STATE)
init_state_params['num_qubits'] = operator.num_qubits
init_state = get_initial_state_instance(init_state_params['name'])
init_state.init_params(init_state_params)
# Set up variational form, we need to add computed num qubits, and initial state to params
var_form_params = params.get(QuantumAlgorithm.SECTION_KEY_VAR_FORM)
var_form_params['num_qubits'] = operator.num_qubits
var_form_params['initial_state'] = init_state
var_form = get_variational_form_instance(var_form_params['name'])
var_form.init_params(var_form_params)
# Set up optimizer
opt_params = params.get(QuantumAlgorithm.SECTION_KEY_OPTIMIZER)
optimizer = get_optimizer_instance(opt_params['name'])
optimizer.init_params(opt_params)
if 'statevector' not in self._backend and operator_mode == 'matrix':
logger.debug('Qasm simulation does not work on {} mode, changing \
the operator_mode to paulis'.format(operator_mode))
operator_mode = 'paulis'
self.init_args(operator,
operator_mode,
var_form,
optimizer,
opt_init_point=initial_point,
aux_operators=algo_input.aux_ops)
logger.info(self.print_setting())
def init_params(self, params, algo_input):
"""
Initialize via parameters dictionary and algorithm input instance
Args:
params: parameters dictionary
algo_input: EnergyInput instance
"""
if algo_input is None:
raise AlgorithmError("EnergyInput instance is required.")
# For getting the extra operator, caller has to do something like: algo_input.add_aux_op(evo_op)
operator = algo_input.qubit_op
aux_ops = algo_input.aux_ops
if aux_ops is None or len(aux_ops) != 1:
raise AlgorithmError(
"EnergyInput, a single aux op is required for evaluation.")
evo_operator = aux_ops[0]
if evo_operator is None:
raise AlgorithmError("EnergyInput, invalid aux op.")
dynamics_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
operator_mode = dynamics_params.get(Dynamics.PROP_OPERATOR_MODE)
evo_time = dynamics_params.get(Dynamics.PROP_EVO_TIME)
num_time_slices = dynamics_params.get(Dynamics.PROP_NUM_TIME_SLICES)
paulis_grouping = dynamics_params.get(Dynamics.PROP_PAULIS_GROUPING)
expansion_mode = dynamics_params.get(Dynamics.PROP_EXPANSION_MODE)
expansion_order = dynamics_params.get(Dynamics.PROP_EXPANSION_ORDER)
# Set up initial state, we need to add computed num qubits to params
initial_state_params = params.get(
QuantumAlgorithm.SECTION_KEY_INITIAL_STATE)
initial_state_params['num_qubits'] = operator.num_qubits
initial_state = get_initial_state_instance(
initial_state_params['name'])
initial_state.init_params(initial_state_params)
self.init_args(operator,
operator_mode,
initial_state,
evo_operator,
evo_time,
num_time_slices,
paulis_grouping=paulis_grouping,
expansion_mode=expansion_mode,
expansion_order=expansion_order)
def init_params(self, params, algo_input):
"""
Initialize via parameters dictionary and algorithm input instance
Args:
params: parameters dictionary
algo_input: EnergyInput instance
"""
if algo_input is None:
raise AlgorithmError("EnergyInput instance is required.")
operator = algo_input.qubit_op
evolution_fidelity_params = params.get(QuantumAlgorithm.SECTION_KEY_ALGORITHM)
expansion_order = evolution_fidelity_params.get(EvolutionFidelity.PROP_EXPANSION_ORDER)
# Set up initial state, we need to add computed num qubits to params
initial_state_params = params.get(QuantumAlgorithm.SECTION_KEY_INITIAL_STATE)
initial_state_params['num_qubits'] = operator.num_qubits
initial_state = get_initial_state_instance(initial_state_params['name'])
initial_state.init_params(initial_state_params)
self.init_args(operator, initial_state, expansion_order)
def test_qpe(self, qubitOp):
self.algorithm = 'QPE'
self.log.debug('Testing QPE')
self.qubitOp = qubitOp
exact_eigensolver = get_algorithm_instance('ExactEigensolver')
exact_eigensolver.init_args(self.qubitOp, k=1)
results = exact_eigensolver.run()
w = results['eigvals']
v = results['eigvecs']
self.qubitOp._check_representation('matrix')
np.testing.assert_almost_equal(self.qubitOp.matrix @ v[0], w[0] * v[0])
np.testing.assert_almost_equal(
expm(-1.j * self.qubitOp.matrix) @ v[0],
np.exp(-1.j * w[0]) * v[0])
self.ref_eigenval = w[0]
self.ref_eigenvec = v[0]
self.log.debug('The exact eigenvalue is: {}'.format(
self.ref_eigenval))
self.log.debug('The corresponding eigenvector: {}'.format(
self.ref_eigenvec))
num_time_slices = 50
num_iterations = 12
iqpe = get_algorithm_instance('IQPE')
iqpe.setup_quantum_backend(backend='local_qasm_simulator',
shots=100,
skip_transpiler=True)
state_in = get_initial_state_instance('CUSTOM')
state_in.init_args(self.qubitOp.num_qubits,
state_vector=self.ref_eigenvec)
iqpe.init_args(
self.qubitOp,
state_in,
num_time_slices,
num_iterations,
paulis_grouping='random',
expansion_mode='suzuki',
expansion_order=2,
)
result = iqpe.run()
# self.log.debug('operator paulis:\n{}'.format(self.qubitOp.print_operators('paulis')))
# self.log.debug('qpe circuit:\n\n{}'.format(result['circuit']['complete'].qasm()))
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 + result['translation']) *
result['stretch'],
max_num_digits=num_iterations + 3,
fractional_part_only=True)))
np.testing.assert_approx_equal(self.ref_eigenval,
result['energy'],
significant=2)
def test_evolution(self):
SIZE = 2
#SPARSITY = 0
#X = [[0, 1], [1, 0]]
#Y = [[0, -1j], [1j, 0]]
Z = [[1, 0], [0, -1]]
I = [[1, 0], [0, 1]]
# + 0.5 * np.kron(Y, X)# + 0.3 * np.kron(Z, X) + 0.4 * np.kron(Z, Y)
h1 = np.kron(I, Z)
# np.random.seed(2)
temp = np.random.random((2**SIZE, 2**SIZE))
h1 = temp + temp.T
qubitOp = Operator(matrix=h1)
# qubitOp_jw.chop_by_threshold(10 ** -10)
if qubitOp.grouped_paulis is None:
qubitOp._matrix_to_paulis()
qubitOp._paulis_to_grouped_paulis()
for ps in qubitOp.grouped_paulis:
for p1 in ps:
for p2 in ps:
if p1 != p2:
np.testing.assert_almost_equal(
p1[1].to_matrix() @ p2[1].to_matrix(),
p2[1].to_matrix() @ p1[1].to_matrix())
state_in = get_initial_state_instance('CUSTOM')
state_in.init_args(SIZE, state='random')
evo_time = 1
num_time_slices = 3
# announces params
self.log.debug('evo time: {}'.format(evo_time))
self.log.debug('num time slices: {}'.format(num_time_slices))
self.log.debug('state_in: {}'.format(state_in._state_vector))
# get the exact state_out from raw matrix multiplication
state_out_exact = qubitOp.evolve(state_in.construct_circuit('vector'),
evo_time, 'matrix', 0)
# self.log.debug('exact:\n{}'.format(state_out_exact))
qubitOp_temp = copy.deepcopy(qubitOp)
for grouping in ['default', 'random']:
self.log.debug('Under {} paulis grouping:'.format(grouping))
for expansion_mode in ['trotter', 'suzuki']:
self.log.debug(
'Under {} expansion mode:'.format(expansion_mode))
for expansion_order in [
1, 2, 3, 4
] if expansion_mode == 'suzuki' else [1]:
# assure every time the operator from the original one
qubitOp = copy.deepcopy(qubitOp_temp)
if expansion_mode == 'suzuki':
self.log.debug(
'With expansion order {}:'.format(expansion_order))
state_out_matrix = qubitOp.evolve(
state_in.construct_circuit('vector'),
evo_time,
'matrix',
num_time_slices,
paulis_grouping=grouping,
expansion_mode=expansion_mode,
expansion_order=expansion_order)
quantum_registers = QuantumRegister(qubitOp.num_qubits)
qc = state_in.construct_circuit('circuit',
quantum_registers)
qc += qubitOp.evolve(
None,
evo_time,
'circuit',
num_time_slices,
quantum_registers=quantum_registers,
paulis_grouping=grouping,
expansion_mode=expansion_mode,
expansion_order=expansion_order,
)
job = q_execute(
qc, qiskit.Aer.get_backend('statevector_simulator'))
state_out_circuit = np.asarray(
job.result().get_statevector(qc))
self.log.debug(
'The fidelity between exact and matrix: {}'.format(
state_fidelity(state_out_exact, state_out_matrix)))
self.log.debug(
'The fidelity between exact and circuit: {}'.format(
state_fidelity(state_out_exact,
state_out_circuit)))
f_mc = state_fidelity(state_out_matrix, state_out_circuit)
self.log.debug(
'The fidelity between matrix and circuit: {}'.format(
f_mc))
self.assertAlmostEqual(f_mc, 1)
def setUp(self):
self.custom = get_initial_state_instance('CUSTOM')
def test_qpe(self, distance):
self.algorithm = 'QPE'
self.log.debug(
'Testing End-to-End with QPE on H2 with inter-atomic distance {}.'.
format(distance))
cfg_mgr = ConfigurationManager()
pyscf_cfg = OrderedDict([('atom',
'H .0 .0 .0; H .0 .0 {}'.format(distance)),
('unit', 'Angstrom'), ('charge', 0),
('spin', 0), ('basis', 'sto3g')])
section = {}
section['properties'] = pyscf_cfg
try:
driver = cfg_mgr.get_driver_instance('PYSCF')
except ModuleNotFoundError:
self.skipTest('PYSCF driver does not appear to be installed')
self.molecule = driver.run(section)
ferOp = FermionicOperator(h1=self.molecule._one_body_integrals,
h2=self.molecule._two_body_integrals)
self.qubitOp = ferOp.mapping(
map_type='PARITY', threshold=1e-10).two_qubit_reduced_operator(2)
exact_eigensolver = get_algorithm_instance('ExactEigensolver')
exact_eigensolver.init_args(self.qubitOp, k=1)
results = exact_eigensolver.run()
self.reference_energy = results['energy']
self.log.debug('The exact ground state energy is: {}'.format(
results['energy']))
num_particles = self.molecule._num_alpha + self.molecule._num_beta
two_qubit_reduction = True
num_orbitals = self.qubitOp.num_qubits + (2 if two_qubit_reduction else
0)
qubit_mapping = 'parity'
num_time_slices = 50
n_ancillae = 9
qpe = get_algorithm_instance('QPE')
qpe.setup_quantum_backend(backend='local_qasm_simulator',
shots=100,
skip_transpiler=True)
state_in = get_initial_state_instance('HartreeFock')
state_in.init_args(self.qubitOp.num_qubits, num_orbitals,
qubit_mapping, two_qubit_reduction, num_particles)
iqft = get_iqft_instance('STANDARD')
iqft.init_args(n_ancillae)
qpe.init_args(self.qubitOp,
state_in,
iqft,
num_time_slices,
n_ancillae,
paulis_grouping='random',
expansion_mode='suzuki',
expansion_order=2)
result = qpe.run()
self.log.debug('measurement results: {}'.format(
result['measurements']))
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 energy from QPE: {}'.format(result['energy']))
self.log.debug('reference energy: {}'.format(
self.reference_energy))
self.log.debug('ref energy (transformed): {}'.format(
(self.reference_energy + result['translation']) *
result['stretch']))
self.log.debug('ref binary str label: {}'.format(
decimal_to_binary((self.reference_energy + result['translation']) *
result['stretch'],
max_num_digits=n_ancillae + 3,
fractional_part_only=True)))
np.testing.assert_approx_equal(result['energy'],
self.reference_energy,
significant=2)
def setUp(self):
self.hf = get_initial_state_instance('HartreeFock')
def setUp(self):
self.zero = get_initial_state_instance('ZERO')
