qml.proc

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.

Internally, these functions dispatch by using the TensorBox class, a container and API for array-like objects that allows array manipulation to be performed in a unified manner for supported tensor/array manipulation frameworks.

The following frameworks are currently supported:

  • NumPy

  • Autograd

  • TensorFlow

  • PyTorch

Functions

T(tensor)

Returns the transpose of the tensor by reversing the order of the axes.

allclose(a, b[, rtol, atol])

Returns True if two arrays are element-wise equal within a tolerance.

allequal(tensor1, tensor2, **kwargs)

Returns True if two tensors are element-wise equal along a given axis.

cast(tensor, dtype)

Casts the given tensor to a new type.

cast_like(tensor1, tensor2)

Casts a tensor to the same dtype as another.

convert_like(tensor1, tensor2)

Convert a tensor to the same type as another.

expand_dims(tensor, axis)

Expand the shape of an array by adding a new dimension of size 1 at the specified axis location.

get_interface(tensor)

Returns the name of the package that any array/tensor manipulations will dispatch to.

ones_like(tensor[, dtype])

Returns a tensor of all ones with the same shape and dtype as the input tensor.

requires_grad(tensor)

Returns True if the tensor is considered trainable.

shape(tensor)

Returns the shape of the tensor.

stack(values[, axis])

Stack a sequence of tensors along the specified axis.

toarray(tensor)

Returns the tensor as a NumPy ndarray.

Classes

TensorBox(tensor)

A container for array-like objects that allows array manipulation to be performed in a unified manner for supported tensor/array manipulation frameworks.