def probabilities(self, qubits=None, measurement_gate=None):
order = (tuple(sorted(qubits)) +
tuple(i for i in range(self.nqubits) if i not in qubits))
order = order + tuple(i + self.nqubits for i in order)
shape = 2 * (2**len(qubits), 2**(self.nqubits - len(qubits)))
state = K.reshape(self.tensor, 2 * self.nqubits * (2, ))
state = K.reshape(K.transpose(state, order), shape)
state = K.einsum("abab->a", state)
return K.reshape(K.cast(state, dtype='DTYPE'), len(qubits) * (2, ))
def test_apply_gate(nqubits, target, dtype, compile, einsum_str):
"""Check that ``K.op.apply_gate`` agrees with einsum gate implementation."""
def apply_operator(state, gate):
qubits = qubits_tensor(nqubits, [target])
return K.op.apply_gate(state, gate, qubits, nqubits, target,
get_threads())
state = random_complex((2**nqubits, ), dtype=dtype)
gate = random_complex((2, 2), dtype=dtype)
target_state = K.reshape(state, nqubits * (2, ))
target_state = K.einsum(einsum_str, target_state, gate)
target_state = target_state.numpy().ravel()
if compile:
apply_operator = K.compile(apply_operator)
state = apply_operator(state, gate)
np.testing.assert_allclose(target_state, state, atol=_atol)
def density_matrix_partial_trace(self, state):
self._set_nqubits(state)
state = K.reshape(state, 2 * self.nqubits * (2, ))
state = K.transpose(state, self.cache.einsum_order)
state = K.reshape(state, self.cache.einsum_shape)
return K.einsum("abac->bc", state)
def __call__(self, cache: str, state: K.Tensor,
gate: K.Tensor) -> K.Tensor:
return K.einsum(cache, state, gate)
