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dq.fock 

fock(dim: int | tuple[int, ...], number: ArrayLike) -> Array

Returns the ket of a Fock state or a tensor product of Fock states.

Parameters

  • dim –

    Hilbert space dimension of each mode.

  • number (array_like of shape (...) or (..., len(dim))) –

    Fock state number for each mode, of integer type. If dim is a tuple, the last dimension of number should match the length of dim.

Returns

(array of shape (..., n, 1)) Ket of the Fock state or tensor product of Fock states, with n = prod(dims).

Examples

Single-mode Fock state \(\ket{1}\):

>>> dq.fock(3, 1)
Array([[0.+0.j],
       [1.+0.j],
       [0.+0.j]], dtype=complex64)

Batched single-mode Fock states \(\{\ket{0}\!, \ket{1}\!, \ket{2}\}\):

>>> dq.fock(3, [0, 1, 2])
Array([[[1.+0.j],
        [0.+0.j],
        [0.+0.j]],

       [[0.+0.j],
        [1.+0.j],
        [0.+0.j]],

       [[0.+0.j],
        [0.+0.j],
        [1.+0.j]]], dtype=complex64)

Multi-mode Fock state \(\ket{1,0}\):

>>> dq.fock((3, 2), (1, 0))
Array([[0.+0.j],
       [0.+0.j],
       [1.+0.j],
       [0.+0.j],
       [0.+0.j],
       [0.+0.j]], dtype=complex64)

Batched multi-mode Fock states \(\{\ket{0,0}\!, \ket{0,1}\!, \ket{1,1}\!, \ket{2,0}\}\):

>>> number = [(0, 0), (0, 1), (1, 1), (2, 0)]
>>> dq.fock((3, 2), number).shape
(4, 6, 1)