tensorcircuit.cons#

Constants and setups

tensorcircuit.cons.contraction_info_decorator(algorithm: Callable[[...], Any]) → Callable[[...], Any][source]#

tensorcircuit.cons.contractor(nodes: List[Any], *, optimizer: Any = <function greedy>, memory_limit: Optional[int] = None, output_edge_order: Optional[List[Any]] = None, ignore_edge_order: bool = False, **kws: Any) → Any#

tensorcircuit.cons.custom(nodes: List[Any], optimizer: Any, memory_limit: Optional[int] = None, output_edge_order: Optional[List[Any]] = None, ignore_edge_order: bool = False, **kws: Any) → Any[source]#

tensorcircuit.cons.custom_stateful(nodes: List[Any], optimizer: Any, memory_limit: Optional[int] = None, opt_conf: Optional[Dict[str, Any]] = None, output_edge_order: Optional[List[Any]] = None, ignore_edge_order: bool = False, **kws: Any) → Any[source]#

tensorcircuit.cons.d2s(n: int, dl: List[Any]) → List[Any][source]#

tensorcircuit.cons.experimental_contractor(nodes: List[Any], output_edge_order: Optional[List[Any]] = None, ignore_edge_order: bool = False, local_steps: int = 2) → Any[source]#

tensorcircuit.cons.get_contractor(method: Optional[str] = None, optimizer: Optional[Any] = None, memory_limit: Optional[int] = None, opt_conf: Optional[Dict[str, Any]] = None, *, set_global: bool = False, contraction_info: bool = False, debug_level: int = 0, **kws: Any) → Callable[[...], Any]#

To set runtime contractor of the tensornetwork for a better contraction path. For more information on the usage of contractor, please refer to independent tutorial.

Parameters

method (Optional[str], optional) – “auto”, “greedy”, “branch”, “plain”, “tng”, “custom”, “custom_stateful”. defaults to None (“auto”)
optimizer (Optional[Any], optional) – Valid for “custom” or “custom_stateful” as method, defaults to None
memory_limit (Optional[int], optional) – It is not very useful, as memory_limit leads to branch contraction instead of greedy which is rather slow, defaults to None

Raises

Exception – Tensornetwork version is too low to support some of the contractors.
ValueError – Unknown method options.

Returns

The new tensornetwork with its contractor set.

Return type

tn.Node

tensorcircuit.cons.get_dtype(dtype: Optional[str] = None, *, set_global: bool = False) → Tuple[str, str]#

Set the global runtime numerical dtype of tensors.

Parameters: dtype (Optional[str], optional) – “complex64”/”float32” or “complex128”/”float64”, defaults to None, which is equivalent to “complex64”.
Returns: complex dtype str and the corresponding real dtype str
Return type: Tuple[str, str]

tensorcircuit.cons.nodes_to_adj(ns: List[Any]) → Any[source]#

tensorcircuit.cons.plain_contractor(nodes: List[Any], output_edge_order: Optional[List[Any]] = None, ignore_edge_order: bool = False) → Any[source]#

The naive state-vector simulator contraction path.

Parameters

nodes (List[Any]) – The list of tn.Node.
output_edge_order (Optional[List[Any]], optional) – The list of dangling node edges, defaults to be None.

Returns

The tn.Node after contraction

Return type

tn.Node

tensorcircuit.cons.runtime_backend(backend: Optional[str] = None) → Iterator[Any][source]#

Context manager to set with-level runtime backend

Parameters: backend (Optional[str], optional) – “numpy”, “tensorflow”, “jax”, “pytorch”, defaults to None
Yield: the backend object
Return type: Iterator[Any]

tensorcircuit.cons.runtime_contractor(*confargs: Any, **confkws: Any) → Iterator[Any][source]#

Context manager to change with-levek contractor

Yield: _description_
Return type: Iterator[Any]

tensorcircuit.cons.runtime_dtype(dtype: Optional[str] = None) → Iterator[Tuple[str, str]][source]#

Context manager to set with-level runtime dtype

Parameters: dtype (Optional[str], optional) – “complex64” or “complex128”, defaults to None (“complex64”)
Yield: complex dtype str and real dtype str
Return type: Iterator[Tuple[str, str]]

tensorcircuit.cons.set_backend(backend: Optional[str] = None, set_global: bool = True) → Any#

To set the runtime backend of tensorcircuit.

Note: tc.set_backend and tc.cons.set_tensornetwork_backend are the same.

Example

>>> tc.set_backend("numpy")
numpy_backend
>>> tc.gates.num_to_tensor(0.1)
array(0.1+0.j, dtype=complex64)
>>>
>>> tc.set_backend("tensorflow")
tensorflow_backend
>>> tc.gates.num_to_tensor(0.1)
<tf.Tensor: shape=(), dtype=complex64, numpy=(0.1+0j)>
>>>
>>> tc.set_backend("pytorch")
pytorch_backend
>>> tc.gates.num_to_tensor(0.1)
tensor(0.1000+0.j)
>>>
>>> tc.set_backend("jax")
jax_backend
>>> tc.gates.num_to_tensor(0.1)
DeviceArray(0.1+0.j, dtype=complex64)

Parameters

backend (Optional[str], optional) – “numpy”, “tensorflow”, “jax”, “pytorch”. defaults to None, which gives the same behavior as tensornetwork.backend_contextmanager.get_default_backend().
set_global (bool) – Whether the object should be set as global.

Returns

The tc.backend object that with all registered universal functions.

Return type

backend object

tensorcircuit.cons.set_contractor(method: Optional[str] = None, optimizer: Optional[Any] = None, memory_limit: Optional[int] = None, opt_conf: Optional[Dict[str, Any]] = None, set_global: bool = True, contraction_info: bool = False, debug_level: int = 0, **kws: Any) → Callable[[...], Any][source]#

To set runtime contractor of the tensornetwork for a better contraction path. For more information on the usage of contractor, please refer to independent tutorial.

Parameters

method (Optional[str], optional) – “auto”, “greedy”, “branch”, “plain”, “tng”, “custom”, “custom_stateful”. defaults to None (“auto”)
optimizer (Optional[Any], optional) – Valid for “custom” or “custom_stateful” as method, defaults to None
memory_limit (Optional[int], optional) – It is not very useful, as memory_limit leads to branch contraction instead of greedy which is rather slow, defaults to None

Raises

Exception – Tensornetwork version is too low to support some of the contractors.
ValueError – Unknown method options.

Returns

The new tensornetwork with its contractor set.

Return type

tn.Node

tensorcircuit.cons.set_dtype(dtype: Optional[str] = None, set_global: bool = True) → Tuple[str, str][source]#

Set the global runtime numerical dtype of tensors.

Parameters: dtype (Optional[str], optional) – “complex64”/”float32” or “complex128”/”float64”, defaults to None, which is equivalent to “complex64”.
Returns: complex dtype str and the corresponding real dtype str
Return type: Tuple[str, str]

tensorcircuit.cons.set_function_backend(backend: Optional[str] = None) → Callable[[...], Any][source]#

Function decorator to set function-level runtime backend

Parameters: backend (Optional[str], optional) – “numpy”, “tensorflow”, “jax”, “pytorch”, defaults to None
Returns: Decorated function
Return type: Callable[…, Any]

tensorcircuit.cons.set_function_contractor(*confargs: Any, **confkws: Any) → Callable[[...], Any][source]#

Function decorate to change function-level contractor

Returns: _description_
Return type: Callable[…, Any]

tensorcircuit.cons.set_function_dtype(dtype: Optional[str] = None) → Callable[[...], Any][source]#

Function decorator to set function-level numerical dtype

Parameters: dtype (Optional[str], optional) – “complex64” or “complex128”, defaults to None
Returns: The decorated function
Return type: Callable[…, Any]

tensorcircuit.cons.set_tensornetwork_backend(backend: Optional[str] = None, set_global: bool = True) → Any[source]#

To set the runtime backend of tensorcircuit.

Note: tc.set_backend and tc.cons.set_tensornetwork_backend are the same.

Example

>>> tc.set_backend("numpy")
numpy_backend
>>> tc.gates.num_to_tensor(0.1)
array(0.1+0.j, dtype=complex64)
>>>
>>> tc.set_backend("tensorflow")
tensorflow_backend
>>> tc.gates.num_to_tensor(0.1)
<tf.Tensor: shape=(), dtype=complex64, numpy=(0.1+0j)>
>>>
>>> tc.set_backend("pytorch")
pytorch_backend
>>> tc.gates.num_to_tensor(0.1)
tensor(0.1000+0.j)
>>>
>>> tc.set_backend("jax")
jax_backend
>>> tc.gates.num_to_tensor(0.1)
DeviceArray(0.1+0.j, dtype=complex64)

Parameters

backend (Optional[str], optional) – “numpy”, “tensorflow”, “jax”, “pytorch”. defaults to None, which gives the same behavior as tensornetwork.backend_contextmanager.get_default_backend().
set_global (bool) – Whether the object should be set as global.

Returns

The tc.backend object that with all registered universal functions.

Return type

backend object

tensorcircuit.cons.split_rules(max_singular_values: Optional[int] = None, max_truncation_err: Optional[float] = None, relative: bool = False) → Any[source]#

Obtain the direcionary of truncation rules

Parameters

max_singular_values (int, optional) – The maximum number of singular values to keep.
max_truncation_err (float, optional) – The maximum allowed truncation error.
relative (bool, optional) – Multiply max_truncation_err with the largest singular value.

tensorcircuit.cons.tn_greedy_contractor(nodes: List[Any], output_edge_order: Optional[List[Any]] = None, ignore_edge_order: bool = False, max_branch: int = 1) → Any[source]#