Source code for pennylane.math

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

#     http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
This package contains unified functions for framework-agnostic tensor and array
manipulation. Given the input tensor-like object, the call is dispatched
to the corresponding array manipulation framework, allowing for end-to-end
differentiation to be preserved.

.. warning::

    These functions are experimental, and only a subset of common functionality is supported.
    Furthermore, the names and behaviour of these functions may differ from similar
    functions in common frameworks; please refer to the function docstrings for more details.

The following frameworks are currently supported:

* NumPy
* Autograd
* TensorFlow
* PyTorch
* JAX
"""
import autoray as ar

from .is_independent import is_independent
from .matrix_manipulation import expand_matrix, reduce_matrices, get_batch_size
from .multi_dispatch import (
    add,
    array,
    block_diag,
    concatenate,
    detach,
    diag,
    dot,
    einsum,
    expm,
    eye,
    frobenius_inner_product,
    gammainc,
    get_trainable_indices,
    iscomplex,
    jax_argnums_to_tape_trainable,
    kron,
    matmul,
    multi_dispatch,
    norm,
    svd,
    ones_like,
    scatter,
    scatter_element_add,
    set_index,
    stack,
    tensordot,
    unwrap,
    where,
)
from .quantum import (
    cov_matrix,
    dm_from_state_vector,
    marginal_prob,
    mutual_info,
    partial_trace,
    purity,
    reduce_dm,
    reduce_statevector,
    relative_entropy,
    sqrt_matrix,
    vn_entropy,
    vn_entanglement_entropy,
    max_entropy,
    min_entropy,
    trace_distance,
)
from .fidelity import fidelity, fidelity_statevector
from .utils import (
    allclose,
    allequal,
    cast,
    cast_like,
    convert_like,
    get_deep_interface,
    get_interface,
    in_backprop,
    is_abstract,
    requires_grad,
)

sum = ar.numpy.sum
toarray = ar.numpy.to_numpy
T = ar.numpy.transpose


[docs]def get_dtype_name(x) -> str: """An interface independent way of getting the name of the datatype. >>> x = tf.Variable(0.1) >>> qml.math.get_dtype_name(tf.Variable(0.1)) 'float32' """ return ar.get_dtype_name(x)
class NumpyMimic(ar.autoray.NumpyMimic): """Subclass of the Autoray NumpyMimic class in order to support the NumPy fft submodule""" # pylint: disable=too-few-public-methods def __getattribute__(self, fn): if fn == "fft": return numpy_fft return super().__getattribute__(fn) numpy_mimic = NumpyMimic() numpy_fft = ar.autoray.NumpyMimic("fft") # small constant for numerical stability that the user can modify eps = 1e-14 def __getattr__(name): return getattr(numpy_mimic, name) __all__ = [ "add", "allclose", "allequal", "array", "block_diag", "cast", "cast_like", "concatenate", "convert_like", "cov_matrix", "detach", "diag", "dm_from_state_vector", "dot", "einsum", "expand_matrix", "eye", "fidelity", "fidelity_statevector", "frobenius_inner_product", "get_dtype_name", "get_interface", "get_deep_interface", "get_trainable_indices", "in_backprop", "is_abstract", "is_independent", "iscomplex", "marginal_prob", "max_entropy", "min_entropy", "multi_dispatch", "mutual_info", "ones_like", "partial_trace", "purity", "reduce_dm", "reduce_statevector", "relative_entropy", "requires_grad", "sqrt_matrix", "scatter_element_add", "stack", "svd", "tensordot", "trace_distance", "unwrap", "vn_entropy", "vn_entanglement_entropy", "where", ]