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.

The following frameworks are currently supported:

• NumPy

• Autograd

• TensorFlow

• PyTorch

• JAX

Functions

_multi_dispatch(values)	Determines the correct framework to dispatch to given a sequence of tensor-like objects.
multi_dispatch([argnum, tensor_list])	Decorater to dispatch arguments handled by the interface.
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.
block_diag(values[, like])	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, like])	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, like])	Construct a diagonal tensor from a list of scalars.
dot(tensor1, tensor2[, like])	Returns the matrix or dot product of two tensors.
frobenius_inner_product(A, B[, normalize, like])	Frobenius inner product between two matrices.
get_interface(tensor)	Returns the name of the package that any array/tensor manipulations will dispatch to.
get_trainable_indices(values[, like])	Returns a set containing the trainable indices of a sequence of values.
is_abstract(tensor[, like])	Returns True if the tensor is considered abstract.
is_independent(func, interface, args[, …])	Test whether a function is independent of its input arguments, both numerically and analytically.
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[, interface])	Returns True if the tensor is considered trainable.
scatter_element_add(tensor, index, value[, like])	In-place addition of a multidimensional value over various indices of a tensor.
stack(values[, axis, like])	Stack a sequence of tensors along the specified axis.
tensordot(tensor1, tensor2[, axes, like])	Returns the tensor product of two tensors.
unwrap(values[, max_depth])	Unwrap a sequence of objects to NumPy arrays.
where(condition[, x, y])	Returns elements chosen from x or y depending on a boolean tensor condition, or the indices of entries satisfying the condition.