qml.math

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.
abs(tensor)	Returns the element-wise absolute value.
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.
angle(tensor)	Returns the element-wise angle of a complex tensor.
arcsin(tensor)	Returns the element-wise inverse sine of the tensor
block_diag(values)	Combine a sequence of 2D tensors to form a block diagonal tensor.
cast(tensor, dtype)	Casts the given tensor to a new type.
cast_like(tensor1, tensor2)	Casts a tensor to the same dtype as another.
concatenate(values[, axis])	Concatenate a sequence of tensors along the specified axis.
convert_like(tensor1, tensor2)	Convert a tensor to the same type as another.
cov_matrix(prob, obs[, wires, diag_approx])	Calculate the covariance matrix of a list of commuting observables, given the joint probability distribution of the system in the shared eigenbasis.
diag(values[, k])	Construct a diagonal tensor from a list of scalars.
dot(tensor1, tensor2)	Returns the matrix or dot product of two tensors.
expand_dims(tensor, axis)	Expand the shape of an array by adding a new dimension of size 1 at the specified axis location.
flatten(tensor)	Flattens an N-dimensional tensor to a 1-dimensional tensor.
gather(tensor, indices)	Gather tensor values given a tuple of indices.
get_interface(tensor)	Returns the name of the package that any array/tensor manipulations will dispatch to.
marginal_prob(prob, axis)	Compute the marginal probability given a joint probability distribution expressed as a tensor.
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.
reshape(tensor, shape)	Gives a new shape to a tensor without changing its data.
scatter_element_add(tensor, index, value)	Adds a scalar value to a specific index of a tensor.
shape(tensor)	Returns the shape of the tensor.
sqrt(tensor)	Returns the element-wise square root.
squeeze(tensor)	Remove single-dimensional entries from the shape of an array.
stack(values[, axis])	Stack a sequence of tensors along the specified axis.
sum(tensor[, axis, keepdims])	TensorBox: Returns the sum of the tensor elements across the specified dimensions.
take(tensor, indices[, axis])	Gather elements from a tensor.
toarray(tensor)	Returns the tensor as a NumPy ndarray.
where(condition, x, y)	Returns elements chosen from x or y depending on a boolean tensor condition.
wrap_output(func)	Decorator to automate the wrapping of TensorBox method outputs.

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.