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 from_params(self, params):
if EnergyInput.PROP_KEY_QUBITOP not in params:
raise AlgorithmError("Qubit operator is required.")
qparams = params[EnergyInput.PROP_KEY_QUBITOP]
self._qubit_op = Operator.load_from_dict(qparams)
if EnergyInput.PROP_KEY_AUXOPS in params:
auxparams = params[EnergyInput.PROP_KEY_AUXOPS]
self._aux_ops = [
Operator.load_from_dict(auxparams[i])
for i in range(len(auxparams))
]
def setUp(self):
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)
"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)
},
"label": "ZX"
}, {
"coeff": {
"imag": 0.0,
"real": 0.1115
},
"label": "ZZ"
}]
}
# Create input variable that specifies we want the energy value
algo_input = get_input_instance("EnergyInput")
# Adds the Hamiltonian information to the input variable
algo_input.qubit_op = Operator.load_from_dict(pauli_dict)
# Defines the algorithm input in terms of the pauli dict and that we want the energy value
# Specifies the attributes of the algorithm that will be used to find the ground state energy
# algorithm: we will use the VQE
# optimiser: best choice for optimisation dependent on the simulated system
# variational_form: the ansatz or first guess at a solution
# depth: the complexity of the circuit used in the algorithm
# backend: the actual device the code will be run on
params = {
"algorithm": {
"name": "VQE"
},
"optimizer": {
"name": "SPSA"
},
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'
},
'problem': {
'name': 'energy',
'random_seed': 50,
'circuit_caching': False,
'skip_qobj_deepcopy': False,
'skip_qobj_validation': False,
'circuit_cache_file': None,
},
'backend': {
'name': backend,
'shots': 1000
},
}
res[backend] = run_algorithm(params_no_caching, self.algo_input)
self.reference_vqe_result = res