def _calculate_unitaries(self):
matrices = K.stack([
self._tfkron(
self.one_qubit_gate(q1, theta=self.params[q1]).unitary,
self.one_qubit_gate(q2, theta=self.params[q2]).unitary)
for q1, q2 in self.pairs
],
axis=0)
entangling_matrix = self.two_qubit_gate(0, 1).unitary
matrices = K.matmul(entangling_matrix, matrices)
additional_matrix = None
q = self.additional_target
if q is not None:
additional_matrix = self.one_qubit_gate(
q, theta=self.params[q]).unitary
if self.params2:
matrices2 = K.stack([
self._tfkron(
self.one_qubit_gate(q1, theta=self.params2[q1]).unitary,
self.one_qubit_gate(q2, theta=self.params2[q2]).unitary)
for q1, q2 in self.pairs
],
axis=0)
matrices = K.matmul(matrices2, matrices)
q = self.additional_target
if q is not None:
additional_matrix = K.matmul(
self.one_qubit_gate(q, theta=self.params2[q]).unitary,
additional_matrix)
return matrices, additional_matrix
def __call__(self, cache: Dict, state: K.Tensor,
gate: K.Tensor) -> K.Tensor:
shapes = cache["shapes"]
state = K.reshape(state, shapes[0])
state = K.transpose(state, cache["ids"])
if cache["conjugate"]:
state = K.reshape(K.conj(state), shapes[1])
else:
state = K.reshape(state, shapes[1])
n = len(tuple(gate.shape))
if n > 2:
dim = 2**(n // 2)
state = K.matmul(K.reshape(gate, (dim, dim)), state)
else:
state = K.matmul(gate, state)
state = K.reshape(state, shapes[2])
state = K.transpose(state, cache["inverse_ids"])
state = K.reshape(state, shapes[3])
return state
def __call__(self, state):
propagator = self.current_hamiltonian.exp(self.dt)
self.t += self.dt
return K.matmul(propagator, state[:, K.newaxis])[:, 0]
def _convert_to_decimal(self):
_range = K.range(self.nqubits - 1, -1, -1, dtype=K.dtypes('DTYPEINT'))
_range = K.pow(2, _range)[:, K.newaxis]
return K.matmul(self.binary, _range)[:, 0]
def density_matrix_call(self, state):
return K.trace(K.matmul(self.hamiltonian.matrix, state))
