def build_controlled_inverse(self,
qc,
q,
q_control,
q_ancillas=None,
params=None):
if params is None:
params = self._params
# get qubits
q_compare = q[params['i_compare']]
q_objective = q[params['i_objective']]
# apply approximate payoff function
cry(-2 * self.offset_angle_zero, q_control, q_objective, qc)
ccry(-2 * self.offset_angle, q_control, q_compare, q_objective, qc)
for i in self._params['i_state']:
multi_cry_q(-2 * self.slope_angle * 2**i,
[q_control, q_compare, q[i]], q_objective, q_ancillas,
qc)
# apply comparator to compare qubit
self._comparator.build_controlled_inverse(qc, q, q_control, q_ancillas,
params)
# apply uncertainty model
self._uncertainty_model.build_controlled_inverse(
qc, q, q_control, q_ancillas, params)
def build(self, qc, q, q_ancillas=None, params=None):
if params is None:
params = self._params
# get qubits
q_objective = q[params['i_objective']]
# apply uncertainty model
self.uncertainty_model.build(qc, q, q_ancillas, params)
# apply approximate payoff function
qc.ry(2 * self.offset_angle, q_objective)
for i in params['i_state']:
cry(2 * self.slope_angle[i], q[i], q_objective, qc)
def build(self, qc, q, q_ancillas=None, params=None):
if params is None:
params = self._params
# get qubits
q_compare = q[params['i_compare']]
q_objective = q[params['i_objective']]
# apply uncertainty model
self._uncertainty_model.build(qc, q, q_ancillas, params)
# apply comparator to compare qubit
self._comparator.build(qc, q, q_ancillas, params)
# apply approximate payoff function
qc.ry(2 * self.offset_angle_zero, q_objective)
cry(2 * self.offset_angle, q_compare, q_objective, qc)
for i in self._params['i_state']:
ccry(2 * self.slope_angle * 2**i, q_compare, q[i], q_objective, qc)
