Source code for thewalrus.decompositions

# Copyright 2019 Xanadu Quantum Technologies Inc.

# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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**Module name:** :mod:`thewalrus.decompositions`

.. currentmodule:: thewalrus.decompositions

This module implements common shared matrix decompositions that are
used to perform gate decompositions.


.. autosummary::

Code details
import numpy as np

from scipy.linalg import sqrtm, schur, polar
from thewalrus.symplectic import sympmat
from thewalrus.quantum.gaussian_checks import is_symplectic

[docs] def williamson(V, rtol=1e-05, atol=1e-08): r"""Williamson decomposition of positive-definite (real) symmetric matrix. See `this thread <>`_ and the `Williamson decomposition documentation <>`_ Args: V (array[float]): positive definite symmetric (real) matrix rtol (float): the relative tolerance parameter used in ``np.allclose`` atol (float): the absolute tolerance parameter used in ``np.allclose`` Returns: tuple[array,array]: ``(Db, S)`` where ``Db`` is a diagonal matrix and ``S`` is a symplectic matrix such that :math:`V = S Db S^T` """ (n, m) = V.shape if n != m: raise ValueError("The input matrix is not square") if not np.allclose(V, V.T, rtol=rtol, atol=atol): raise ValueError("The input matrix is not symmetric") if n % 2 != 0: raise ValueError("The input matrix must have an even number of rows/columns") n = n // 2 omega = sympmat(n) vals = np.linalg.eigvalsh(V) if not np.all(vals > 0): raise ValueError("Input matrix is not positive definite") M12 = np.real_if_close(sqrtm(V)) Mm12 = np.linalg.inv(M12) Gamma = Mm12 @ omega @ Mm12 a, O = schur(Gamma) # In what follows a permutation matrix perm is constructed so that the Schur matrix has # only positive elements above the diagonal # Also the Schur matrix uses the x_1,p_1, ..., x_n,p_n ordering thus the permutation perm is updated # to go to the ordering x_1, ..., x_n, p_1, ... , p_n perm = np.arange(2 * n) for i in range(n): if a[2 * i, 2 * i + 1] <= 0: (perm[2 * i], perm[2 * i + 1]) = (perm[2 * i + 1], perm[2 * i]) perm = np.array([perm[2 * i] for i in range(n)] + [perm[2 * i + 1] for i in range(n)]) O = O[:, perm] phi = np.abs(np.diag(a, k=1)[::2]) dd = np.concatenate([1 / phi, 1 / phi]) ddsqrt = 1 / np.sqrt(dd) S = M12 @ O * ddsqrt return np.diag(dd), S
[docs] def symplectic_eigenvals(cov): r"""Returns the symplectic eigenvalues of a covariance matrix. Args: cov (array): a covariance matrix Returns: (array): symplectic eigenvalues """ M = len(cov) // 2 Omega = sympmat(M) return np.real_if_close(-1j * np.linalg.eigvals(Omega @ cov))[::2]
[docs] def blochmessiah(S): """Returns the Bloch-Messiah decomposition of a symplectic matrix S = O @ D @ Q where O and Q are orthogonal symplectic matrices and D is a positive-definite diagonal matrix of the form diag(d1,d2,...,dn,d1^-1, d2^-1,...,dn^-1), Args: S (array[float]): 2N x 2N real symplectic matrix Returns: tuple(array[float], : orthogonal symplectic matrix O array[float], : diagonal matrix D array[float]) : orthogonal symplectic matrix Q """ N, _ = S.shape if not is_symplectic(S): raise ValueError("Input matrix is not symplectic.") N = N // 2 V, P = polar(S, side="left") A = P[:N, :N] B = P[:N, N:] C = P[N:, N:] M = A - C + 1j * (B + B.T) Lam, W = takagi(M) Lam = 0.5 * Lam O = np.block([[W.real, -W.imag], [W.imag, W.real]]) Q = O.T @ V sqrt1pLam2 = np.sqrt(1 + Lam**2) D = np.diag(np.concatenate([sqrt1pLam2 + Lam, sqrt1pLam2 - Lam])) return O, D, Q
[docs] def takagi(A, svd_order=True): r"""Autonne-Takagi decomposition of a complex symmetric (not Hermitian!) matrix. Note that the input matrix is internally symmetrized by taking its upper triangular part. If the input matrix is indeed symmetric this leaves it unchanged. See `Carl Caves note. <>`_ Args: A (array): square, symmetric matrix svd_order (boolean): whether to return result by ordering the singular values of ``A`` in descending (``True``) or ascending (``False``) order. Returns: tuple[array, array]: (r, U), where r are the singular values, and U is the Autonne-Takagi unitary, such that :math:`A = U \diag(r) U^T`. """ n, m = A.shape if n != m: raise ValueError("The input matrix is not square") # Here we build a Symmetric matrix from the top right triangular part A = np.triu(A) + np.triu(A, k=1).T A = np.real_if_close(A) if np.allclose(A, 0): return np.zeros(n), np.eye(n) if np.isrealobj(A): # If the matrix A is real one can be more clever and use its eigendecomposition ls, U = np.linalg.eigh(A) vals = np.abs(ls) # These are the Takagi eigenvalues signs = (-1) ** (1 + np.heaviside(ls, 1)) phases = np.sqrt(np.complex128(signs)) Uc = U * phases # One needs to readjust the phases # Find the permutation to sort in decreasing order perm = np.argsort(vals) # if svd_order reverse it if svd_order: perm = perm[::-1] return vals[perm], Uc[:, perm] # Find the element with the largest absolute value pos = np.unravel_index(np.argmax(np.abs(A)), (n, n)) # Use it to find whether the input is a global phase times a real matrix phi = np.angle(A[pos]) Amr = np.real_if_close(np.exp(-1j * phi) * A) if np.isrealobj(Amr): vals, U = takagi(Amr, svd_order=svd_order) return vals, U * np.exp(1j * phi / 2) u, d, v = np.linalg.svd(A) U = u @ sqrtm((v @ np.conjugate(u)).T) if svd_order is False: return d[::-1], U[:, ::-1] return d, U


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