def from_params(cls, params):
if EnergyInput.PROP_KEY_QUBITOP not in params:
raise AquaError("Qubit operator is required.")
qparams = params[EnergyInput.PROP_KEY_QUBITOP]
qubit_op = Operator.load_from_dict(qparams)
if EnergyInput.PROP_KEY_AUXOPS in params:
auxparams = params[EnergyInput.PROP_KEY_AUXOPS]
aux_ops = [Operator.load_from_dict(auxparams[i]) for i in range(len(auxparams))]
return cls(qubit_op, aux_ops)
def setUp(self):
super().setUp()
np.random.seed(50)
pauli_dict = {
'paulis': [{"coeff": {"imag": 0.0, "real": -1.052373245772859}, "label": "II"},
{"coeff": {"imag": 0.0, "real": 0.39793742484318045}, "label": "IZ"},
{"coeff": {"imag": 0.0, "real": -0.39793742484318045}, "label": "ZI"},
{"coeff": {"imag": 0.0, "real": -0.01128010425623538}, "label": "ZZ"},
{"coeff": {"imag": 0.0, "real": 0.18093119978423156}, "label": "XX"}
]
}
qubit_op = Operator.load_from_dict(pauli_dict)
self.algo_input = EnergyInput(qubit_op)
backends = ['statevector_simulator', 'qasm_simulator']
res = {}
for backend in backends:
params_no_caching = {
'algorithm': {'name': 'VQE', 'operator_mode': 'matrix' if backend == 'statevector_simulator' else 'paulis'},
'problem': {'name': 'energy',
'random_seed': 50,
'circuit_caching': False,
'skip_qobj_deepcopy': False,
'skip_qobj_validation': False,
'circuit_cache_file': None,
},
'backend': {'provider': 'qiskit.BasicAer', 'name': backend, 'shots': 1000},
}
qiskit_aqua = QiskitAqua(params_no_caching, self.algo_input)
res[backend] = qiskit_aqua.run()
self.reference_vqe_result = res
def setUp(self):
super().setUp()
np.random.seed(50)
pauli_dict = {
'paulis': [{"coeff": {"imag": 0.0, "real": -1.052373245772859}, "label": "II"},
{"coeff": {"imag": 0.0, "real": 0.39793742484318045}, "label": "IZ"},
{"coeff": {"imag": 0.0, "real": -0.39793742484318045}, "label": "ZI"},
{"coeff": {"imag": 0.0, "real": -0.01128010425623538}, "label": "ZZ"},
{"coeff": {"imag": 0.0, "real": 0.18093119978423156}, "label": "XX"}
]
}
qubit_op = Operator.load_from_dict(pauli_dict)
self.algo_input = EnergyInput(qubit_op)
def getPauliMatrix(matrix):
rows = matrix.shape[0]
paulis = []
for row_pos in range(rows):
for col_pos in range(matrix.shape[1]):
temp = {}
temp["imag"] = 0.0
temp["real"] = matrix[row_pos, col_pos]
label_pauli = ["I" for _ in range(rows)]
if row_pos != col_pos:
label_pauli[row_pos] = 'Z'
label_pauli[col_pos] = 'Z'
label_pauli = "".join(label_pauli)
paulis.append({"coeff": temp, "label": label_pauli})
paulis_dict = {"paulis": paulis}
for i in paulis_dict["paulis"]:
print(i)
pauli_matrix = Operator.load_from_dict(paulis_dict)
return pauli_matrix
"label": "ZI"
}, {
"coeff": {
"imag": 0.0,
"real": -0.01128010425623538
},
"label": "ZZ"
}, {
"coeff": {
"imag": 0.0,
"real": 0.18093119978423156
},
"label": "XX"
}]
}
qubitOp_h2_with_2_qubit_reduction = Operator.load_from_dict(pauli_dict)
class TestIQPE(QiskitAquaTestCase):
"""IQPE tests."""
@parameterized.expand([
[qubitOp_simple, 'qasm_simulator'],
[qubitOp_h2_with_2_qubit_reduction, 'statevector_simulator'],
])
def test_iqpe(self, qubitOp, simulator):
self.algorithm = 'IQPE'
self.log.debug('Testing IQPE')
self.qubitOp = qubitOp
exact_eigensolver = ExactEigensolver(self.qubitOp, k=1)
# Here an Operator instance is created for our Hamiltonian. In this case the paulis are from a previously computed Hamiltonian for simplicity
pauli_dict = {
'paulis': [
{
"coeff": {
"imag": 0.0,
"real": 1
},
"label": "Z"
},
]
}
qubitOp = Operator.load_from_dict(pauli_dict)
print(qubitOp)
algorithm_cfg = {'name': 'VQE', 'operator_mode': 'matrix'}
optimizer_cfg = {'name': 'L_BFGS_B', 'maxfun': 1000}
var_form_cfg = {'name': 'RYRZ', 'depth': 3, 'entanglement': 'linear'}
params = {
'algorithm': algorithm_cfg,
'optimizer': optimizer_cfg,
'variational_form': var_form_cfg,
'backend': {
'name': 'statevector_simulator'
}
