def __new__(cls, *args, **kwargs):
cgate_only = {"I", "M", "Flatten", "CallbackGate", "ZPow", "CZPow"}
if BACKEND.get('GATES') == 'custom' or cls.__name__ in cgate_only:
return super(TensorflowGate, cls).__new__(cls)
else:
from qibo.tensorflow import gates
return getattr(gates, cls.__name__)(*args, **kwargs)
def __init__(self):
self.calculation_cache = None
# For `controlled_by` gates (see `cache.ControlCache` for more details)
self.control_cache = None
# Gate matrices
self.matrix = None
# Einsum backend
self.einsum = BACKEND.get('EINSUM')
def __new__(cls, *args, **kwargs):
cgate_only = {"I", "M", "Flatten", "CallbackGate", "ZPow", "CZPow"}
# TODO: Move these to a different file and refactor
if BACKEND.get('GATES') == 'custom' or cls.__name__ in cgate_only:
return super(TensorflowGate, cls).__new__(cls)
else:
from qibo.tensorflow import gates
return getattr(gates, cls.__name__)(*args, **kwargs) # pylint: disable=E0110
def __new__(cls, *args, **kwargs):
if BACKEND.get('GATES') == 'custom': # pragma: no cover
# future TODO
raise_error(
NotImplementedError, "Density matrices are not supported by "
"custom operator gates.")
else:
from qibo.tensorflow import gates
return getattr(gates, cls.__name__)(*args, **kwargs)
def _one_qubit_matrix(self, gate0: "Gate", gate1: "Gate"):
"""Calculates Kroneker product of two one-qubit gates.
Args:
gate0: Gate that acts on ``self.qubit0``.
gate1: Gate that acts on ``self.qubit1``.
Returns:
4x4 matrix that corresponds to the Kronecker product of the 2x2
gate matrices.
"""
if BACKEND.get('GATES') == "custom":
return np.kron(gate0.unitary, gate1.unitary)
else:
matrix = tf.tensordot(gate0.unitary, gate1.unitary, axes=0)
matrix = tf.transpose(matrix, [0, 2, 1, 3])
return tf.reshape(matrix, (4, 4))
def __new__(cls, q, t1, t2, time, excited_population=0, seed=None):
if BACKEND.get('GATES') == "custom":
cls_a = _ThermalRelaxationChannelA
cls_b = _ThermalRelaxationChannelB
else:
from qibo.tensorflow import gates
cls_a = gates._ThermalRelaxationChannelA
cls_b = gates._ThermalRelaxationChannelB
if t2 > t1:
cls_s = cls_b
else:
cls_s = cls_a
return cls_s(q,
t1,
t2,
time,
excited_population=excited_population,
seed=seed)
def __new__(cls, q0, q1, theta):
if BACKEND.get('GATES') == 'custom':
return CU1(q0, q1, theta)
else:
from qibo.tensorflow import gates
return gates.CU1(q0, q1, theta)
def __init__(self):
super(FusionGroup, self).__init__()
self.bk = np
if BACKEND.get('GATES') != "custom":
self.bk = tf
def __new__(cls, q, theta, trainable=True):
if BACKEND.get('GATES') == 'custom':
return U1(q, theta, trainable)
else:
from qibo.tensorflow import gates
return gates.U1(q, theta, trainable)