mm.math.backend_manager.BackendManager¶

class mrmustard.math.backend_manager.BackendManager[source]¶

Bases: object

A class to manage the different backends supported by Mr Mustard.

Attributes

`backend`	The backend that is being used.
`backend_name`	The name of the backend in use.
`euclidean_opt`	The configured Euclidean optimizer.
`instance`

backend¶: The backend that is being used.

backend_name¶: The name of the backend in use.

euclidean_opt¶: The configured Euclidean optimizer.

instance = Backend(numpy)¶

Methods

`Categorical`(probs, name)	Categorical distribution over integers.
`DefaultEuclideanOptimizer`()	Default optimizer for the Euclidean parameters.
`J`(num_modes)	Symplectic form.
`MultivariateNormalTriL`(loc, scale_tril)	Multivariate normal distribution on R^k and parameterized by a (batch of) length-k loc vector (aka "mu") and a (batch of) k x k scale matrix; covariance = scale @ scale.T where @ denotes matrix-multiplication.
`Xmat`(num_modes)	The matrix \(X_n = \begin{bmatrix}0 & I_n\\ I_n & 0\end{bmatrix}.\)
`Zmat`(num_modes)	The matrix \(Z_n = \begin{bmatrix}I_n & 0\\ 0 & -I_n\end{bmatrix}.\)
`abs`(array)	The absolute value of array.
`add_at_modes`(old, new, modes)	Adds two phase-space tensors (cov matrices, displacement vectors, etc..) on the specified modes.
`all_diagonals`(rho, real)	Returns all the diagonals of a density matrix.
`allclose`(array1, array2[, atol])	Whether two arrays are equal within tolerance.
`any`(array)	Returns `True` if any element of array is `True`, `False` otherwise.
`arange`(start[, limit, delta, dtype])	Returns an array of evenly spaced values within a given interval.
`asnumpy`(tensor)	Converts an array to a numpy array.
`assign`(tensor, value)	Assigns value to tensor.
`astensor`(array[, dtype])	Converts a numpy array to a tensor.
`atleast_1d`(array[, dtype])	Returns an array with at least one dimension.
`atleast_2d`(array[, dtype])	Returns an array with at least two dimensions.
`atleast_3d`(array[, dtype])	Returns an array with at least three dimensions by eventually inserting new axes at the beginning.
`binomial_conditional_prob`(success_prob, ...)	\(P(out\|in) = binom(in, out) * (1-success_prob)*(in-out) success_prob**out\).
`block`(blocks[, axes])	Returns a matrix made from the given blocks.
`block_diag`(mat1, mat2)	Returns a block diagonal matrix from the given matrices.
`boolean_mask`(tensor, mask)	Returns a tensor based on the truth value of the boolean mask.
`cast`(array[, dtype])	Casts `array` to `dtype`.
`change_backend`(name)	Changes the backend to a different one.
`cholesky`(input)	Computes the Cholesky decomposition of square matrices.
`clip`(array, a_min, a_max)	Clips array to the interval `[a_min, a_max]`.
`concat`(values, axis)	Concatenates values along the given axis.
`conj`(array)	The complex conjugate of array.
`constraint_func`(bounds)	Returns a constraint function for the given bounds.
`convolution`(array, filters[, padding, ...])	Performs a convolution on array with filters.
`convolve_probs`(prob, other)	Convolve two probability distributions (up to 3D) with the same shape.
`convolve_probs_1d`(prob, other_probs)	Convolution of a joint probability with a list of single-index probabilities.
`cos`(array)	The cosine of an array.
`cosh`(array)	The hyperbolic cosine of array.
`custom_gradient`(func)	A decorator to define a function with a custom gradient.
`dagger`(array)	The adjoint of `array`.
`det`(matrix)	The determinant of matrix.
`diag`(array[, k])	The array made by inserting the given array along the \(k\)-th diagonal.
`diag_part`(array[, k])	The array of the main diagonal of array.
`eigh`(tensor)	The eigenvalues and eigenvectors of a matrix.
`eigvals`(tensor)	The eigenvalues of a tensor.
`einsum`(string, *tensors)	The result of the Einstein summation convention on the tensors.
`euclidean_to_symplectic`(S, dS_euclidean)	Convert the Euclidean gradient to a Riemannian gradient on the tangent bundle of the symplectic manifold.
`euclidean_to_unitary`(U, dU_euclidean)	Convert the Euclidean gradient to a Riemannian gradient on the tangent bundle of the unitary manifold.
`exp`(array)	The exponential of array element-wise.
`expand_dims`(array, axis)	The array with an additional dimension inserted at the given axis.
`expm`(matrix)	The matrix exponential of matrix.
`eye`(size[, dtype])	The identity matrix of size.
`eye_like`(array)	The identity matrix of the same shape and dtype as array.
`from_backend`(value)	Whether the given tensor is a tensor of the concrete backend.
`gather`(array, indices[, axis])	The values of the array at the given indices.
`hermite_renormalized_1leftoverMode`(A, B, C, ...)	First, reorder A and B parameters of Bargmann representation to match conventions in mrmustard.math.compactFock~ Then, calculate the required renormalized multidimensional Hermite polynomial.
`hermite_renormalized_batch`(A, B, C, shape)	Renormalized multidimensional Hermite polynomial given by the "exponential" Taylor series of \(exp(C + Bx + 1/2*Ax^2)\) at zero, where the series has \(sqrt(n!)\) at the denominator rather than \(n!\).
`hermite_renormalized_diagonal`(A, B, C, cutoffs)	Firsts, reorder A and B parameters of Bargmann representation to match conventions in mrmustard.math.compactFock~ Then, calculates the required renormalized multidimensional Hermite polynomial.
`hermite_renormalized_diagonal_batch`(A, B, C, ...)	First, reorder A and B parameters of Bargmann representation to match conventions in mrmustard.math.compactFock~ Then, calculates the required renormalized multidimensional Hermite polynomial.
`imag`(array)	The imaginary part of array.
`inv`(tensor)	The inverse of tensor.
`is_trainable`(tensor)	Whether the given tensor is trainable.
`kron`(tensor1, tensor2)	The Kroenecker product of the given tensors.
`left_matmul_at_modes`(a_partial, b_full, modes)	Left matrix multiplication of a partial matrix and a full matrix.
`lgamma`(x)	The natural logarithm of the gamma function of `x`.
`log`(x)	The natural logarithm of `x`.
`make_complex`(real, imag)	Given two real tensors representing the real and imaginary part of a complex number, this operation returns a complex tensor.
`map_fn`(fn, elements)	Transforms elems by applying fn to each element unstacked on axis 0.
`matmul`(*matrices)	The matrix product of the given matrices.
`matvec`(a, b)	The matrix vector product of `a` (matrix) and `b` (vector).
`matvec_at_modes`(mat, vec, modes)	Matrix-vector multiplication between a phase-space matrix and a vector in the specified modes.
`maximum`(a, b)	The element-wise maximum of `a` and `b`.
`minimum`(a, b)	The element-wise minimum of `a` and `b`.
`new_constant`(value, name[, dtype])	Returns a new constant with the given value.
`new_variable`(value, bounds, name[, dtype])	Returns a new variable with the given value and bounds.
`norm`(array)	The norm of array.
`ones`(shape[, dtype])	Returns an array of ones with the given `shape` and `dtype`.
`ones_like`(array)	Returns an array of ones with the same shape and `dtype` as `array`.
`outer`(array1, array2)	The outer product of `array1` and `array2`.
`pad`(array, paddings[, mode, constant_values])	The padded array.
`pinv`(matrix)	The pseudo-inverse of matrix.
`poisson`(max_k, rate)	Poisson distribution up to `max_k`.
`pow`(x, y)	Returns \(x^y\).
`prod`(array[, axis])	The product of all elements in `array`.
`random_orthogonal`(N)	A random orthogonal matrix in \(O(N)\).
`random_symplectic`(num_modes[, max_r])	A random symplectic matrix in `Sp(2*num_modes)`.
`random_unitary`(N)	a random unitary matrix in \(U(N)\)
`real`(array)	The real part of `array`.
`reshape`(array, shape)	The reshaped array.
`right_matmul_at_modes`(a_full, b_partial, modes)	Right matrix multiplication of a full matrix and a partial matrix.
`rotmat`(num_modes)	Rotation matrix from quadratures to complex amplitudes.
`round`(array, decimals)	The array rounded to the nearest integer.
`set_diag`(array, diag, k)	The array with the diagonal set to `diag`.
`sin`(array)	The sine of `array`.
`single_mode_to_multimode_mat`(mat, num_modes)	Apply the same \(2\times 2\) matrix (i.e. single-mode) to a larger number of modes.
`single_mode_to_multimode_vec`(vec, num_modes)	Apply the same 2-vector (i.e. single-mode) to a larger number of modes.
`sinh`(array)	The hyperbolic sine of `array`.
`solve`(matrix, rhs)	The solution of the linear system \(Ax = b\).
`sort`(array[, axis])	Sort the array along an axis.
`sqrt`(x[, dtype])	The square root of `x`.
`sqrtm`(tensor[, dtype])	The matrix square root.
`squeeze`(tensor, axis)	Removes dimensions of size 1 from the shape of a tensor.
`sum`(array[, axes])	The sum of array.
`tensordot`(a, b, axes)	The tensordot product of `a` and `b`.
`tile`(array, repeats)	The tiled array.
`trace`(array[, dtype])	The trace of array.
`transpose`(a[, perm])	The transposed arrays.
`unitary_to_orthogonal`(U)	Unitary to orthogonal mapping.
`update_add_tensor`(tensor, indices, values)	Updates a tensor in place by adding the given values.
`update_tensor`(tensor, indices, values)	Updates a tensor in place with the given values.
`value_and_gradients`(cost_fn, parameters)	The loss and gradients of the given cost function.
`xlogy`(x, y)	Returns `0` if `x == 0` elementwise and `x * log(y)` otherwise.
`zeros`(shape[, dtype])	Returns an array of zeros with the given shape and `dtype`.
`zeros_like`(array)	Returns an array of zeros with the same shape and `dtype` as `array`.

Categorical(probs, name)[source]¶

Categorical distribution over integers.

Parameters:

probs (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The unnormalized probabilities of a set of Categorical distributions.
name (str) – The name prefixed to operations created by this class.

Returns:

instance of tfp.distributions.Categorical class

Return type:

tfp.distributions.Categorical

DefaultEuclideanOptimizer()[source]¶: Default optimizer for the Euclidean parameters.

static J(num_modes)[source]¶: Symplectic form.

MultivariateNormalTriL(loc, scale_tril)[source]¶

Multivariate normal distribution on R^k and parameterized by a (batch of) length-k loc vector (aka “mu”) and a (batch of) k x k scale matrix; covariance = scale @ scale.T where @ denotes matrix-multiplication.

Parameters:

loc (Tensor) – if this is set to None, loc is implicitly 0
scale_tril (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – lower-triangular Tensor with non-zero diagonal elements

Returns:

instance of tfp.distributions.MultivariateNormalTriL

Return type:

tfp.distributions.MultivariateNormalTriL

static Xmat(num_modes)[source]¶

The matrix \(X_n = \begin{bmatrix}0 & I_n\\ I_n & 0\end{bmatrix}.\)

Parameters:: num_modes (int) – positive integer
Returns:: The \(2N\times 2N\) array

static Zmat(num_modes)[source]¶

The matrix \(Z_n = \begin{bmatrix}I_n & 0\\ 0 & -I_n\end{bmatrix}.\)

Parameters:: num_modes (int) – A positive integer representing the number of modes.
Returns:: The \(2N\times 2N\) array

abs(array)[source]¶

The absolute value of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the absolute value of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The absolute value of the given array.

add_at_modes(old, new, modes)[source]¶

Adds two phase-space tensors (cov matrices, displacement vectors, etc..) on the specified modes.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

all_diagonals(rho, real)[source]¶

Returns all the diagonals of a density matrix.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

allclose(array1, array2, atol=1e-09)[source]¶

Whether two arrays are equal within tolerance.

The two arrays are compaired element-wise.

Parameters:

array1 (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – An array.
array2 (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – Another array.
atol – The absolute tolerance.

Return type:

bool

Returns:

Whether two arrays are equal within tolerance.

Raises:

ValueError – If the shape of the two arrays do not match.

any(array)[source]¶

Returns True if any element of array is True, False otherwise.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to check.
Return type:: bool
Returns:: True if any element of array is True, False otherwise.

arange(start, limit=None, delta=1, dtype=None)[source]¶

Returns an array of evenly spaced values within a given interval.

Parameters:

start (int) – The start of the interval.
limit (int) – The end of the interval.
delta (int) – The step size.
dtype (Any) – The dtype of the returned array.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array of evenly spaced values.

asnumpy(tensor)[source]¶

Converts an array to a numpy array.

Parameters:: tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to convert.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The corresponidng numpy array.

assign(tensor, value)[source]¶

Assigns value to tensor.

Parameters:

tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to assign to.
value (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The value to assign.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The tensor with value assigned

astensor(array, dtype=None)[source]¶

Converts a numpy array to a tensor.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The numpy array to convert.
dtype – The dtype of the tensor. If None, the returned tensor is of type float.

Returns:

The tensor with dtype.

atleast_1d(array, dtype=None)[source]¶

Returns an array with at least one dimension.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to convert.
dtype – The data type of the array. If None, the returned array is of the same type as the given one.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array with at least one dimension.

atleast_2d(array, dtype=None)[source]¶

Returns an array with at least two dimensions.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to convert.
dtype – The data type of the array. If None, the returned array is of the same type as the given one.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array with at least two dimensions.

atleast_3d(array, dtype=None)[source]¶

Returns an array with at least three dimensions by eventually inserting new axes at the beginning. Note this is not the way atleast_3d works in numpy and tensorflow, where it adds at the beginning and/or end.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to convert.
dtype – The data type of the array. If None, the returned array is of the same type as the given one.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array with at least three dimensions.

binomial_conditional_prob(success_prob, dim_out, dim_in)[source]¶: \(P(out|in) = binom(in, out) * (1-success_prob)**(in-out) * success_prob**out\).

block(blocks, axes=(-2, -1))[source]¶

Returns a matrix made from the given blocks.

Parameters:

blocks (List[List[ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]]]) – A list of lists of compatible blocks.
axes – The axes to stack the blocks along.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The matrix made of blocks.

block_diag(mat1, mat2)[source]¶

Returns a block diagonal matrix from the given matrices.

Parameters:

mat1 (ndarray[Tuple[int, int], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – A matrix.
mat2 (ndarray[Tuple[int, int], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – A matrix.

Return type:

ndarray[Tuple[int, int], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

A block diagonal matrix from the given matrices.

boolean_mask(tensor, mask)[source]¶

Returns a tensor based on the truth value of the boolean mask.

Parameters:

tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – A tensor.
mask (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – A boolean mask.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

A tensor based on the truth value of the boolean mask.

cast(array, dtype=None)[source]¶

Casts array to dtype.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to cast.
dtype – The data type to cast to. If None, the returned array is the same as the given one.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array cast to dtype.

change_backend(name)[source]¶

Changes the backend to a different one.

Parameters:: name (str) – The name of the new backend.
Return type:: None

cholesky(input)[source]¶

Computes the Cholesky decomposition of square matrices.

Parameters:: input (Tensor) –
Returns:: tensor with the same type as input
Return type:: Tensor

clip(array, a_min, a_max)[source]¶

Clips array to the interval [a_min, a_max].

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to clip.
a_min (float) – The minimum value.
a_max (float) – The maximum value.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The clipped array.

concat(values, axis)[source]¶

Concatenates values along the given axis.

Parameters:

values (Sequence[ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]]) – The values to concatenate.
axis (int) – The axis along which to concatenate.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The concatenated values.

conj(array)[source]¶

The complex conjugate of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the complex conjugate of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The complex conjugate of the given array.

constraint_func(bounds)[source]¶

Returns a constraint function for the given bounds.

A constraint function will clip the value to the interval given by the bounds.

Note

The upper and/or lower bounds can be None, in which case the constraint function will not clip the value.

Parameters:: bounds (Tuple[Optional[float], Optional[float]]) – The bounds of the constraint.
Return type:: Optional[Callable]
Returns:: The constraint function.

convolution(array, filters, padding=None, data_format='NWC')[source]¶

Performs a convolution on array with filters.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to convolve.
filters (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The filters to convolve with.
padding (Optional[str]) – The padding mode.
data_format – The data format of the array.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The convolved array.

convolve_probs(prob, other)[source]¶

Convolve two probability distributions (up to 3D) with the same shape.

Note that the output is not guaranteed to be a complete joint probability, as it’s computed only up to the dimension of the base probs.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

convolve_probs_1d(prob, other_probs)[source]¶

Convolution of a joint probability with a list of single-index probabilities.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

cos(array)[source]¶

The cosine of an array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the cosine of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The cosine of array.

cosh(array)[source]¶

The hyperbolic cosine of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the hyperbolic cosine of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The hyperbolic cosine of array.

custom_gradient(func)[source]¶: A decorator to define a function with a custom gradient.

dagger(array)[source]¶

The adjoint of array. This operation swaps the first and second half of the indexes and then conjugates the matrix.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the adjoint of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The adjoint of array

det(matrix)[source]¶

The determinant of matrix.

Parameters:: matrix (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The matrix to take the determinant of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The determinant of matrix.

diag(array, k=0)[source]¶

The array made by inserting the given array along the \(k\)-th diagonal.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to insert.
k (int) – The k-th diagonal to insert array into.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array with array inserted into the k-th diagonal.

diag_part(array, k=0)[source]¶

The array of the main diagonal of array.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to extract the main diagonal of.
k (int) – The diagonal to extract.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array of the main diagonal of array.

eigh(tensor)[source]¶

The eigenvalues and eigenvectors of a matrix.

Parameters:: tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to calculate the eigenvalues and eigenvectors of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The eigenvalues and eigenvectors of tensor.

eigvals(tensor)[source]¶

The eigenvalues of a tensor.

Parameters:: tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to calculate the eigenvalues of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The eigenvalues of tensor.

einsum(string, *tensors)[source]¶

The result of the Einstein summation convention on the tensors.

Parameters:

string (str) – The string of the Einstein summation convention.
tensors – The tensors to perform the Einstein summation on.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The result of the Einstein summation convention.

euclidean_to_symplectic(S, dS_euclidean)[source]¶

Convert the Euclidean gradient to a Riemannian gradient on the tangent bundle of the symplectic manifold.

Implemented from:: Wang J, Sun H, Fiori S. A Riemannian‐steepest‐descent approach for optimization on the real symplectic group. Mathematical Methods in the Applied Sciences. 2018 Jul 30;41(11):4273-86.

Parameters:

S (Matrix) – symplectic matrix
dS_euclidean (Matrix) – Euclidean gradient tensor

Returns:

symplectic gradient tensor

Return type:

Matrix

euclidean_to_unitary(U, dU_euclidean)[source]¶

Convert the Euclidean gradient to a Riemannian gradient on the tangent bundle of the unitary manifold.

Implemented from:: Y Yao, F Miatto, N Quesada - arXiv preprint arXiv:2209.06069, 2022.

Parameters:

U (Matrix) – unitary matrix
dU_euclidean (Matrix) – Euclidean gradient tensor

Returns:

unitary gradient tensor

Return type:

Matrix

exp(array)[source]¶

The exponential of array element-wise.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the exponential of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The exponential of array.

expand_dims(array, axis)[source]¶

The array with an additional dimension inserted at the given axis.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to expand.
axis (int) – The axis to insert the new dimension.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array with an additional dimension inserted at the given axis.

expm(matrix)[source]¶

The matrix exponential of matrix.

Parameters:: matrix (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The matrix to take the exponential of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The exponential of matrix.

eye(size, dtype=None)[source]¶

The identity matrix of size.

Parameters:

size (int) – The size of the identity matrix
dtype – The data type of the identity matrix. If None, the returned matrix is of type float.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The identity matrix.

eye_like(array)[source]¶

The identity matrix of the same shape and dtype as array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to create the identity matrix of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The identity matrix.

from_backend(value)[source]¶

Whether the given tensor is a tensor of the concrete backend.

Parameters:: value (Any) – A value.
Return type:: bool
Returns:: Whether given value is a tensor of the concrete backend.

gather(array, indices, axis=None)[source]¶

The values of the array at the given indices.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to gather values from.
indices (Batch[int]) – The indices to gather values from.
axis (Optional[int]) – The axis to gather values from.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The values of the array at the given indices.

hermite_renormalized_1leftoverMode(A, B, C, cutoffs)[source]¶

First, reorder A and B parameters of Bargmann representation to match conventions in mrmustard.math.compactFock~ Then, calculate the required renormalized multidimensional Hermite polynomial.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

hermite_renormalized_batch(A, B, C, shape)[source]¶

Renormalized multidimensional Hermite polynomial given by the “exponential” Taylor series of \(exp(C + Bx + 1/2*Ax^2)\) at zero, where the series has \(sqrt(n!)\) at the denominator rather than \(n!\). It computes all the amplitudes within the tensor of given shape in case of B is a batched vector with a batched diemnsion on the last index.

Parameters:

A (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The A matrix.
B (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The batched B vector with its batch dimension on the last index.
C (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The C scalar.
shape (Tuple[int]) – The shape of the final tensor.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The batched Hermite polynomial of given shape.

hermite_renormalized_diagonal(A, B, C, cutoffs)[source]¶

Firsts, reorder A and B parameters of Bargmann representation to match conventions in mrmustard.math.compactFock~ Then, calculates the required renormalized multidimensional Hermite polynomial.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

hermite_renormalized_diagonal_batch(A, B, C, cutoffs)[source]¶

First, reorder A and B parameters of Bargmann representation to match conventions in mrmustard.math.compactFock~ Then, calculates the required renormalized multidimensional Hermite polynomial. Same as hermite_renormalized_diagonal but works for a batch of different B’s.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

imag(array)[source]¶

The imaginary part of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the imaginary part of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The imaginary part of array

inv(tensor)[source]¶

The inverse of tensor.

Parameters:: tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to take the inverse of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The inverse of tensor

is_trainable(tensor)[source]¶

Whether the given tensor is trainable.

Parameters:: tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to train.
Return type:: bool
Returns:: Whether the given tensor can be trained.

kron(tensor1, tensor2)[source]¶

The Kroenecker product of the given tensors.

Parameters:

tensor1 (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – A tensor.
tensor2 (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – Another tensor.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The Kroenecker product.

left_matmul_at_modes(a_partial, b_full, modes)[source]¶

Left matrix multiplication of a partial matrix and a full matrix.

It assumes that that a_partial is a matrix operating on M modes and that modes is a list of M integers, i.e., it will apply a_partial on the corresponding M modes of b_full from the left.

Parameters:

a_partial (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The \(2M\times 2M\) array
b_full (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The \(2N\times 2N\) array
modes (Sequence[int]) – A list of M modes to perform the multiplication on

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The \(2N\times 2N\) array

lgamma(x)[source]¶

The natural logarithm of the gamma function of x.

Parameters:: x (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the natural logarithm of the gamma function of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The natural logarithm of the gamma function of x

log(x)[source]¶

The natural logarithm of x.

Parameters:: x (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the natural logarithm of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The natural logarithm of x

make_complex(real, imag)[source]¶

Given two real tensors representing the real and imaginary part of a complex number, this operation returns a complex tensor. The input tensors must have the same shape.

Parameters:

real (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The real part of the complex number.
imag (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The imaginary part of the complex number.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The complex array real + 1j * imag.

map_fn(fn, elements)[source]¶

Transforms elems by applying fn to each element unstacked on axis 0.

Parameters:

fn (func) – The callable to be performed. It accepts one argument, which will have the same (possibly nested) structure as elems.
elements (Tensor) – A tensor or (possibly nested) sequence of tensors, each of which will be unstacked along their first dimension. func will be applied to the nested sequence of the resulting slices.

Returns:

applied func on elements

Return type:

Tensor

matmul(*matrices)[source]¶

The matrix product of the given matrices.

Parameters:: matrices (ndarray[Tuple[int, int], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The matrices to multiply.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The matrix product

matvec(a, b)[source]¶

The matrix vector product of a (matrix) and b (vector).

Parameters:

a (ndarray[Tuple[int, int], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The matrix to multiply
b (ndarray[Tuple[int], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The vector to multiply

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The matrix vector product of a and b

matvec_at_modes(mat, vec, modes)[source]¶

Matrix-vector multiplication between a phase-space matrix and a vector in the specified modes.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

maximum(a, b)[source]¶

The element-wise maximum of a and b.

Parameters:

a (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The first array to take the maximum of
b (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The second array to take the maximum of

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The element-wise maximum of a and b

minimum(a, b)[source]¶

The element-wise minimum of a and b.

Parameters:

a (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The first array to take the minimum of
b (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The second array to take the minimum of

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The element-wise minimum of a and b

new_constant(value, name, dtype=None)[source]¶

Returns a new constant with the given value.

Parameters:

value (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The value of the new constant
name (str) – name of the new constant
dtype (type) – dtype of the array

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The new constant

new_variable(value, bounds, name, dtype=None)[source]¶

Returns a new variable with the given value and bounds.

Parameters:

value (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The value of the new variable.
bounds (Tuple[Optional[float], Optional[float]]) – The bounds of the new variable.
name (str) – The name of the new variable.
dtype – dtype of the new variable. If None, casts it to float.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The new variable.

norm(array)[source]¶

The norm of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the norm of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The norm of array

ones(shape, dtype=None)[source]¶

Returns an array of ones with the given shape and dtype.

Parameters:

shape (tuple) – shape of the array
dtype (type) – dtype of the array. If None, the returned array is of type float.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array of ones

ones_like(array)[source]¶

Returns an array of ones with the same shape and dtype as array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the shape and dtype of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The array of ones

outer(array1, array2)[source]¶

The outer product of array1 and array2.

Parameters:

array1 (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The first array to take the outer product of
array2 (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The second array to take the outer product of

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The outer product of array1 and array2

pad(array, paddings, mode='CONSTANT', constant_values=0)[source]¶

The padded array.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to pad
paddings (tuple) – paddings to apply
mode (str) – mode to apply the padding
constant_values (int) – constant values to use for padding

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The padded array

pinv(matrix)[source]¶

The pseudo-inverse of matrix.

Parameters:: matrix (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The matrix to take the pseudo-inverse of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The pseudo-inverse of matrix

poisson(max_k, rate)[source]¶

Poisson distribution up to max_k.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

pow(x, y)[source]¶

Returns \(x^y\). Broadcasts x and y if necessary. :type x: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]] :param x: The base :type y: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]] :param y: The exponent

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The \(x^y\)

prod(array, axis=None)[source]¶

The product of all elements in array.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array of elements to calculate the product of.
axis – The axis along which a product is performed. If None, it calculates the product of all elements in array.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The product of the elements in array.

static random_orthogonal(N)[source]¶

A random orthogonal matrix in \(O(N)\).

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

random_symplectic(num_modes, max_r=1.0)[source]¶

A random symplectic matrix in Sp(2*num_modes).

Squeezing is sampled uniformly from 0.0 to max_r (1.0 by default).

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

random_unitary(N)[source]¶

a random unitary matrix in \(U(N)\)

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

real(array)[source]¶

The real part of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the real part of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The real part of array

reshape(array, shape)[source]¶

The reshaped array.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to reshape
shape (tuple) – shape to reshape the array to

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The reshaped array

right_matmul_at_modes(a_full, b_partial, modes)[source]¶

Right matrix multiplication of a full matrix and a partial matrix.

It assumes that that b_partial is a matrix operating on M modes and that modes is a list of M integers, i.e., it will apply b_partial on the corresponding M modes of a_full from the right.

Parameters:

a_full (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The \(2N\times 2N\) array
b_partial (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The \(2M\times 2M\) array
modes (Sequence[int]) – A list of M modes to perform the multiplication on

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The \(2N\times 2N\) array

static rotmat(num_modes)[source]¶: Rotation matrix from quadratures to complex amplitudes.

round(array, decimals)[source]¶

The array rounded to the nearest integer.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to round
decimals (int) – number of decimals to round to

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array rounded to the nearest integer

set_diag(array, diag, k)[source]¶

The array with the diagonal set to diag.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to set the diagonal of
diag (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The diagonal to set
k (int) – diagonal to set

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array with the diagonal set to diag

sin(array)[source]¶

The sine of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the sine of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The sine of array

single_mode_to_multimode_mat(mat, num_modes)[source]¶: Apply the same \(2\times 2\) matrix (i.e. single-mode) to a larger number of modes.

single_mode_to_multimode_vec(vec, num_modes)[source]¶: Apply the same 2-vector (i.e. single-mode) to a larger number of modes.

sinh(array)[source]¶

The hyperbolic sine of array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the hyperbolic sine of
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The hyperbolic sine of array

solve(matrix, rhs)[source]¶

The solution of the linear system \(Ax = b\).

Parameters:

matrix (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The matrix \(A\)
rhs (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The vector \(b\)

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The solution \(x\)

sort(array, axis=-1)[source]¶

Sort the array along an axis.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to sort
axis (int) – (optional) The axis to sort along. Defaults to last axis.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

A sorted version of the array in acending order.

sqrt(x, dtype=None)[source]¶

The square root of x.

Parameters:

x (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the square root of
dtype – dtype of the output array.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The square root of x

sqrtm(tensor, dtype=None)[source]¶

The matrix square root.

Parameters:

tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to take the matrix square root of.
dtype – The dtype of the output tensor. If None, the output is of type math.complex128.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The square root of x

squeeze(tensor, axis)[source]¶

Removes dimensions of size 1 from the shape of a tensor.

Parameters:

tensor (Tensor) – the tensor to squeeze
axis (Optional[List[int]]) – if specified, only squeezes the dimensions listed, defaults to []

Returns:

tensor with one or more dimensions of size 1 removed

Return type:

Tensor

sum(array, axes=None)[source]¶

The sum of array.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the sum of
axes (tuple) – axes to sum over

Returns:

The sum of array

tensordot(a, b, axes)[source]¶

The tensordot product of a and b.

Parameters:

a (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The first array to take the tensordot product of
b (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The second array to take the tensordot product of
axes (Sequence[int]) – The axes to take the tensordot product over

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The tensordot product of a and b

tile(array, repeats)[source]¶

The tiled array.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to tile
repeats (tuple) – number of times to tile the array along each axis

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The tiled array

trace(array, dtype=None)[source]¶

The trace of array.

Parameters:

array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the trace of
dtype (type) – dtype of the output array

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The trace of array

transpose(a, perm=None)[source]¶

The transposed arrays.

Parameters:

a (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to transpose
perm (tuple) – permutation to apply to the array

Returns:

The transposed array

unitary_to_orthogonal(U)[source]¶

Unitary to orthogonal mapping.

Parameters:: U – The unitary matrix in U(n)
Returns:: The orthogonal matrix in \(O(2n)\)

update_add_tensor(tensor, indices, values)[source]¶

Updates a tensor in place by adding the given values.

Parameters:

tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to update
indices (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The indices to update
values (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The values to add

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The updated tensor

update_tensor(tensor, indices, values)[source]¶

Updates a tensor in place with the given values.

Parameters:

tensor (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The tensor to update
indices (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The indices to update
values (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The values to update

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The updated tensor

value_and_gradients(cost_fn, parameters)[source]¶

The loss and gradients of the given cost function.

Parameters:

cost_fn (callable) – cost function to compute the loss and gradients of
parameters (dict) – parameters to compute the loss and gradients of

Returns:

loss and gradients (dict) of the given cost function

Return type:

tuple

xlogy(x, y)[source]¶

Returns 0 if x == 0 elementwise and x * log(y) otherwise.

Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

zeros(shape, dtype=None)[source]¶

Returns an array of zeros with the given shape and dtype.

Parameters:

shape (Sequence[int]) – The shape of the array.
dtype – The dtype of the array. If None, the returned array is of type float.

Return type:

ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]

Returns:

The array of zeros.

zeros_like(array)[source]¶

Returns an array of zeros with the same shape and dtype as array.

Parameters:: array (ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]) – The array to take the shape and dtype of.
Return type:: ndarray[Tuple[int, ...], Union[TypeVar(R, float16, float32, float64), TypeVar(C, complex64, complex128), TypeVar(Z, int16, int32, int64), TypeVar(N, uint16, uint32, uint64)]]
Returns:: The array of zeros.

code/api/mrmustard.math.backend_manager.BackendManager

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