def test_opflow_qnn_2_2(self, config):
"""Test Opflow QNN with input/output dimension 2/2."""
q_i, input_grad_required = config
if q_i == STATEVECTOR:
quantum_instance = self.sv_quantum_instance
elif q_i == QASM:
quantum_instance = self.qasm_quantum_instance
else:
quantum_instance = None
# construct parametrized circuit
params_1 = [Parameter("input1"), Parameter("weight1")]
qc_1 = QuantumCircuit(1)
qc_1.h(0)
qc_1.ry(params_1[0], 0)
qc_1.rx(params_1[1], 0)
qc_sfn_1 = StateFn(qc_1)
# construct cost operator
h_1 = StateFn(PauliSumOp.from_list([("Z", 1.0), ("X", 1.0)]))
# combine operator and circuit to objective function
op_1 = ~h_1 @ qc_sfn_1
# construct parametrized circuit
params_2 = [Parameter("input2"), Parameter("weight2")]
qc_2 = QuantumCircuit(1)
qc_2.h(0)
qc_2.ry(params_2[0], 0)
qc_2.rx(params_2[1], 0)
qc_sfn_2 = StateFn(qc_2)
# construct cost operator
h_2 = StateFn(PauliSumOp.from_list([("Z", 1.0), ("X", 1.0)]))
# combine operator and circuit to objective function
op_2 = ~h_2 @ qc_sfn_2
op = ListOp([op_1, op_2])
qnn = OpflowQNN(
op,
[params_1[0], params_2[0]],
[params_1[1], params_2[1]],
quantum_instance=quantum_instance,
)
qnn.input_gradients = input_grad_required
test_data = [np.array([1, 2]), np.array([[1, 2], [3, 4]])]
# test model
self.validate_output_shape(qnn, test_data)
# test the qnn after we set a quantum instance
if quantum_instance is None:
qnn.quantum_instance = self.qasm_quantum_instance
self.validate_output_shape(qnn, test_data)
def test_opflow_qnn_2_1(self, config):
"""Test Opflow QNN with input/output dimension 2/1."""
q_i, input_grad_required = config
# construct QNN
if q_i == STATEVECTOR:
quantum_instance = self.sv_quantum_instance
elif q_i == QASM:
quantum_instance = self.qasm_quantum_instance
else:
quantum_instance = None
# specify how to evaluate expected values and gradients
expval = PauliExpectation()
gradient = Gradient()
# construct parametrized circuit
params = [
Parameter("input1"),
Parameter("input2"),
Parameter("weight1"),
Parameter("weight2"),
]
qc = QuantumCircuit(2)
qc.h(0)
qc.ry(params[0], 0)
qc.ry(params[1], 1)
qc.rx(params[2], 0)
qc.rx(params[3], 1)
qc_sfn = StateFn(qc)
# construct cost operator
cost_operator = StateFn(PauliSumOp.from_list([("ZZ", 1.0), ("XX", 1.0)]))
# combine operator and circuit to objective function
op = ~cost_operator @ qc_sfn
# define QNN
qnn = OpflowQNN(
op,
params[:2],
params[2:],
expval,
gradient,
quantum_instance=quantum_instance,
)
qnn.input_gradients = input_grad_required
test_data = [np.array([1, 2]), np.array([[1, 2]]), np.array([[1, 2], [3, 4]])]
# test model
self.validate_output_shape(qnn, test_data)
# test the qnn after we set a quantum instance
if quantum_instance is None:
qnn.quantum_instance = self.qasm_quantum_instance
self.validate_output_shape(qnn, test_data)