tensorcircuit.interfaces.tensortrans#

general function for interfaces transformation

tensorcircuit.interfaces.tensortrans.args_to_tensor(f: Callable[[...], Any], argnums: Union[int, Sequence[int]] = 0, tensor_as_matrix: bool = False, gate_to_tensor: bool = False, gate_as_matrix: bool = True, qop_to_tensor: bool = False, qop_as_matrix: bool = True, cast_dtype: bool = True) → Callable[[...], Any][source]#

Function decorator that automatically convert inputs to tensors on current backend

Example

tc.set_backend("jax")

@partial(
tc.interfaces.args_to_tensor,
argnums=[0, 1, 2],
gate_to_tensor=True,
qop_to_tensor=True,
)
def f(a, b, c, d):
    return a, b, c, d

f(
[tc.Gate(np.ones([2, 2])), tc.Gate(np.ones([2, 2, 2, 2]))],
tc.QuOperator.from_tensor(np.ones([2, 2, 2, 2, 2, 2])),
np.ones([2, 2, 2, 2]),
tf.zeros([1, 2]),
)

# ([DeviceArray([[1.+0.j, 1.+0.j],
#        [1.+0.j, 1.+0.j]], dtype=complex64),
# DeviceArray([[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j]], dtype=complex64)],
# DeviceArray([[1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j],
#             [1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j, 1.+0.j,
#             1.+0.j]], dtype=complex64),
# DeviceArray([[[[1.+0.j, 1.+0.j],
#                 [1.+0.j, 1.+0.j]],

#             [[1.+0.j, 1.+0.j],
#                 [1.+0.j, 1.+0.j]]],


#             [[[1.+0.j, 1.+0.j],
#                 [1.+0.j, 1.+0.j]],

#             [[1.+0.j, 1.+0.j],
#                 [1.+0.j, 1.+0.j]]]], dtype=complex64),
# <tf.Tensor: shape=(1, 2), dtype=float32, numpy=array([[0., 0.]], dtype=float32)>)
Parameters

  • f (Callable[..., Any]) – the wrapped function whose arguments in argnums position are expected to be tensor format

  • argnums (Union[int, Sequence[int]], optional) – position of args under the auto conversion, defaults to 0

  • tensor_as_matrix (bool, optional) – try reshape all input tensor as matrix with shape rank 2, defaults to False

  • gate_to_tensor (bool, optional) – convert Gate to tensor, defaults to False

  • gate_as_matrix (bool, optional) – reshape tensor from Gate input as matrix, defaults to True

  • qop_to_tensor (bool, optional) – convert QuOperator to tensor, defaults to False

  • qop_as_matrix (bool, optional) – reshape tensor from QuOperator input as matrix, defaults to True

  • cast_dtype (bool, optional) – whether cast to backend dtype, defaults to True

Returns

The wrapped function

Return type

Callable[…, Any]

tensorcircuit.interfaces.tensortrans.gate_to_matrix(t: tensorcircuit.gates.Gate, is_reshapem: bool = True) → Any[source]#
tensorcircuit.interfaces.tensortrans.general_args_to_backend(args: Any, dtype: Optional[Any] = None, target_backend: Optional[Any] = None, enable_dlpack: bool = True) → Any[source]#
tensorcircuit.interfaces.tensortrans.general_args_to_numpy(args: Any) → Any[source]#

Given a pytree, get the corresponding numpy array pytree

Parameters

args (Any) – pytree

Returns

the same format pytree with all tensor replaced by numpy array

Return type

Any

tensorcircuit.interfaces.tensortrans.numpy_args_to_backend(args: Any, dtype: Optional[Any] = None, target_backend: Optional[Any] = None) → Any[source]#

Given a pytree of numpy arrays, get the corresponding tensor pytree

Parameters

  • args (Any) – pytree of numpy arrays

  • dtype (Any, optional) – str of str of the same pytree shape as args, defaults to None

  • target_backend (Any, optional) – str or backend object, defaults to None, indicating the current default backend

Returns

the same format pytree with all numpy array replaced by the tensors in the target backend

Return type

Any

tensorcircuit.interfaces.tensortrans.numpy_to_tensor(t: Any, backend: Any) → Any[source]#
tensorcircuit.interfaces.tensortrans.qop_to_matrix(t: tensorcircuit.quantum.QuOperator, is_reshapem: bool = True) → Any[source]#
tensorcircuit.interfaces.tensortrans.tensor_to_backend_jittable(t: Any) → Any[source]#
tensorcircuit.interfaces.tensortrans.tensor_to_dlpack(t: Any) → Any[source]#
tensorcircuit.interfaces.tensortrans.tensor_to_dtype(t: Any) → str[source]#
tensorcircuit.interfaces.tensortrans.tensor_to_numpy(t: Any) → Any[source]#
tensorcircuit.interfaces.tensortrans.which_backend(a: Any, return_backend: bool = True) → Any[source]#

Given a tensor a, return the corresponding backend

Parameters

  • a (Tensor) – the tensor

  • return_backend (bool, optional) – if true, return backend object, if false, return backend str, defaults to True

Returns

the backend object or backend str

Return type

Any