# Source code for strawberryfields.decompositions

# Copyright 2019 Xanadu Quantum Technologies Inc.

# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at

# Unless required by applicable law or agreed to in writing, software
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
"""
This module implements common shared matrix decompositions that are
used to perform gate decompositions.
"""

from itertools import groupby

import numpy as np
from scipy.linalg import block_diag, sqrtm, polar, schur

from .backends.shared_ops import sympmat, changebasis

[docs]def takagi(N, tol=1e-13, rounding=13):
r"""Autonne-Takagi decomposition of a complex symmetric (not Hermitian!) matrix.

Note that singular values of N are considered equal if they are equal after np.round(values, tol).

See :cite:cariolaro2016 and references therein for a derivation.

Args:
N (array[complex]): square, symmetric matrix N
rounding (int): the number of decimal places to use when rounding the singular values of N
tol (float): the tolerance used when checking if the input matrix is symmetric: :math:|N-N^T| < tol

Returns:
tuple[array, array]: (rl, U), where rl are the (rounded) singular values,
and U is the Takagi unitary, such that :math:N = U \diag(rl) U^T.
"""
(n, m) = N.shape
if n != m:
raise ValueError("The input matrix must be square")
if np.linalg.norm(N - np.transpose(N)) >= tol:
raise ValueError("The input matrix is not symmetric")

N = np.real_if_close(N)

if np.allclose(N, 0):
return np.zeros(n), np.eye(n)

if np.isrealobj(N):
# If the matrix N is real one can be more clever and use its eigendecomposition
l, U = np.linalg.eigh(N)
vals = np.abs(l)  # These are the Takagi eigenvalues
phases = np.sqrt(np.complex128([1 if i > 0 else -1 for i in l]))
Uc = U @ np.diag(phases)  # One needs to readjust the phases
list_vals = [(vals[i], i) for i in range(len(vals))]
list_vals.sort(reverse=True)
sorted_l, permutation = zip(*list_vals)
permutation = np.array(permutation)
Uc = Uc[:, permutation]
# And also rearrange the unitary and values so that they are decreasingly ordered
return np.array(sorted_l), Uc

v, l, ws = np.linalg.svd(N)
w = np.transpose(np.conjugate(ws))
rl = np.round(l, rounding)

# Generate list with degenerancies
result = []
for k, g in groupby(rl):
result.append(list(g))

# Generate lists containing the columns that correspond to degenerancies
kk = 0
for k in result:
for ind, j in enumerate(k):  # pylint: disable=unused-variable
k[ind] = kk
kk = kk + 1

# Generate the lists with the degenerate column subspaces
vas = []
was = []
for i in result:
vas.append(v[:, i])
was.append(w[:, i])

# Generate the matrices qs of the degenerate subspaces
qs = []
for i in range(len(result)):
qs.append(sqrtm(np.transpose(vas[i]) @ was[i]))

# Construct the Takagi unitary
qb = block_diag(*qs)

U = v @ np.conj(qb)
return rl, U

[docs]def graph_embed_deprecated(A, max_mean_photon=1.0, make_traceless=False, rtol=1e-05, atol=1e-08):
r"""Embed a graph into a Gaussian state.

Note: The default behaviour of graph embedding has been changed; see :func:graph_embed. This version is deprecated, but has been kept for consistency.

Given a graph in terms of a symmetric adjacency matrix
(in general with arbitrary complex off-diagonal and real diagonal entries),
returns the squeezing parameters and interferometer necessary for
creating the Gaussian state whose off-diagonal parts are proportional to that matrix.

Uses :func:takagi.

Args:
A (array[complex]): square, symmetric (weighted) adjacency matrix of the graph
max_mean_photon (float): Threshold value. It guarantees that the mode with
the largest squeezing has max_mean_photon as the mean photon number
i.e., :math:sinh(r_{max})^2 == :code:max_mean_photon.
make_traceless (bool): Removes the trace of the input matrix, by performing the transformation
:math:\tilde{A} = A-\mathrm{tr}(A) \I/n. This may reduce the amount of squeezing needed to encode
the graph but will lead to different photon number statistics for events with more than
one photon in any mode.
rtol (float): relative tolerance used when checking if the input matrix is symmetric
atol (float): absolute tolerance used when checking if the input matrix is symmetric

Returns:
tuple[array, array]: squeezing parameters of the input
state to the interferometer, and the unitary matrix representing the interferometer
"""
(m, n) = A.shape

if m != n:
raise ValueError("The matrix is not square.")

if not np.allclose(A, np.transpose(A), rtol=rtol, atol=atol):
raise ValueError("The matrix is not symmetric.")

if make_traceless:
A = A - np.trace(A) * np.identity(n) / n

s, U = takagi(A, tol=atol)
sc = np.sqrt(1.0 + 1.0 / max_mean_photon)
vals = -np.arctanh(s / (s[0] * sc))
return vals, U

[docs]def graph_embed(A, mean_photon_per_mode=1.0, make_traceless=False, rtol=1e-05, atol=1e-08):
r"""Embed a graph into a Gaussian state.

Given a graph in terms of a symmetric adjacency matrix
(in general with arbitrary complex entries),
returns the squeezing parameters and interferometer necessary for
creating the Gaussian state whose off-diagonal parts are proportional to that matrix.

Uses :func:takagi.

Args:
A (array[complex]): square, symmetric (weighted) adjacency matrix of the graph
mean_photon_per_mode (float): guarantees that the mean photon number in the pure Gaussian state
representing the graph satisfies  :math:\frac{1}{N}\sum_{i=1}^N sinh(r_{i})^2 == :code:mean_photon
make_traceless (bool): Removes the trace of the input matrix, by performing the transformation
:math:\tilde{A} = A-\mathrm{tr}(A) \I/n. This may reduce the amount of squeezing needed to encode
the graph but will lead to different photon number statistics for events with more than
one photon in any mode.
rtol (float): relative tolerance used when checking if the input matrix is symmetric
atol (float): absolute tolerance used when checking if the input matrix is symmetric

Returns:
tuple[array, array]: squeezing parameters of the input
state to the interferometer, and the unitary matrix representing the interferometer
"""
(m, n) = A.shape

if m != n:
raise ValueError("The matrix is not square.")

if not np.allclose(A, np.transpose(A), rtol=rtol, atol=atol):
raise ValueError("The matrix is not symmetric.")

if make_traceless:
A = A - np.trace(A) * np.identity(n) / n

scale = find_scaling_adjacency_matrix(A, n * mean_photon_per_mode)
A = scale * A
s, U = takagi(A, tol=atol)
vals = -np.arctanh(s)
return vals, U

[docs]def bipartite_graph_embed(A, mean_photon_per_mode=1.0, rtol=1e-05, atol=1e-08):
r"""Embed a bipartite graph into a Gaussian state.

Given a bipartite graph in terms of an adjacency matrix
(in general with arbitrary complex entries),
returns the two-mode squeezing parameters and interferometers necessary for
creating the Gaussian state that encodes such adjacency matrix

Uses :func:takagi.

Args:
A (array[complex]): square, (weighted) adjacency matrix of the bipartite graph
mean_photon_per_mode (float): guarantees that the mean photon number in the pure Gaussian state
representing the graph satisfies  :math:\frac{1}{N}\sum_{i=1}^N sinh(r_{i})^2 == :code:mean_photon
rtol (float): relative tolerance used when checking if the input matrix is symmetric
atol (float): absolute tolerance used when checking if the input matrix is symmetric

Returns:
tuple[array, array, array]: squeezing parameters of the input
state to the interferometer, and the unitaries matrix representing the interferometer
"""
(m, n) = A.shape

if m != n:
raise ValueError("The matrix is not square.")

B = np.block([[0 * A, A], [A.T, 0 * A]])
scale = find_scaling_adjacency_matrix(B, 2 * n * mean_photon_per_mode)
A = scale * A

if np.allclose(A, A.T, rtol=rtol, atol=atol):
s, u = takagi(A, tol=atol)
v = u
else:
u, s, v = np.linalg.svd(A)
v = v.T

vals = -np.arctanh(s)
return vals, u, v

[docs]def T(m, n, theta, phi, nmax):
r"""The Clements T matrix from Eq. 1 of the paper"""
mat = np.identity(nmax, dtype=np.complex128)
mat[m, m] = np.exp(1j * phi) * np.cos(theta)
mat[m, n] = -np.sin(theta)
mat[n, m] = np.exp(1j * phi) * np.sin(theta)
mat[n, n] = np.cos(theta)
return mat

[docs]def Ti(m, n, theta, phi, nmax):
r"""The inverse Clements T matrix"""
return np.transpose(T(m, n, theta, -phi, nmax))

[docs]def nullTi(m, n, U):
r"""Nullifies element m,n of U using Ti"""
(nmax, mmax) = U.shape

if nmax != mmax:
raise ValueError("U must be a square matrix")

if U[m, n] == 0:
thetar = 0
phir = 0
elif U[m, n + 1] == 0:
thetar = np.pi / 2
phir = 0
else:
r = U[m, n] / U[m, n + 1]
thetar = np.arctan(np.abs(r))
phir = np.angle(r)

return [n, n + 1, thetar, phir, nmax]

[docs]def nullT(n, m, U):
r"""Nullifies element n,m of U using T"""
(nmax, mmax) = U.shape

if nmax != mmax:
raise ValueError("U must be a square matrix")

if U[n, m] == 0:
thetar = 0
phir = 0
elif U[n - 1, m] == 0:
thetar = np.pi / 2
phir = 0
else:
r = -U[n, m] / U[n - 1, m]
thetar = np.arctan(np.abs(r))
phir = np.angle(r)

return [n - 1, n, thetar, phir, nmax]

[docs]def rectangular(V, tol=1e-11):
r"""Rectangular decomposition of a unitary matrix, with local
phase shifts applied between two interferometers.

See :ref:rectangular or :cite:clements2016 for more details.

This function returns a circuit corresponding to an intermediate step in
the decomposition as described in Eq. 4 of the article. In this form,
the circuit comprises some T matrices (as in Eq. 1), then phases on all modes,
and more T matrices.

The procedure to construct these matrices is detailed in the supplementary
material of the article.

Args:
V (array[complex]): unitary matrix of size n_size
tol (float): the tolerance used when checking if the matrix is unitary:
:math:|VV^\dagger-I| \leq tol

Returns:
tuple[array]: tuple of the form (tilist,np.diag(localV),tlist)
where:

* tilist: list containing [n,m,theta,phi,n_size] of the Ti unitaries needed
* tlist: list containing [n,m,theta,phi,n_size] of the T unitaries needed
* localV: Diagonal unitary sitting sandwiched by Ti's and the T's
"""
localV = V
(nsize, _) = localV.shape

diffn = np.linalg.norm(V @ V.conj().T - np.identity(nsize))
if diffn >= tol:
raise ValueError("The input matrix is not unitary")

tilist = []
tlist = []
for k, i in enumerate(range(nsize - 2, -1, -1)):
if k % 2 == 0:
for j in reversed(range(nsize - 1 - i)):
tilist.append(nullTi(i + j + 1, j, localV))
localV = localV @ Ti(*tilist[-1])
else:
for j in range(nsize - 1 - i):
tlist.append(nullT(i + j + 1, j, localV))
localV = T(*tlist[-1]) @ localV

return tilist, np.diag(localV), tlist

[docs]def rectangular_phase_end(V, tol=1e-11):
r"""Rectangular decomposition of a unitary matrix, with all
local phase shifts placed after the interferometers.

See :cite:clements2016 for more details.

Final step in the decomposition of a given discrete unitary matrix.
The output is of the form given in Eq. 5.

Args:
V (array[complex]): unitary matrix of size n_size
tol (float): the tolerance used when checking if the matrix is unitary:
:math:|VV^\dagger-I| \leq tol

Returns:
tuple[array]: returns a tuple of the form (tlist, np.diag(localV), None)
where:

* tlist: list containing [n,m,theta,phi,n_size] of the T unitaries needed
* localV: Diagonal unitary matrix to be applied at the end of circuit
"""
tilist, diags, tlist = rectangular(V, tol)
new_tlist, new_diags = tilist.copy(), diags.copy()

# Push each beamsplitter through the diagonal unitary
for i in reversed(tlist):
em, en = int(i[0]), int(i[1])
alpha, beta = np.angle(new_diags[em]), np.angle(new_diags[en])
theta, phi = i[2], i[3]

# The new parameters required for D',T' st. T^(-1)D = D'T'
new_theta = theta
new_phi = (alpha - beta + np.pi) % (2 * np.pi)
new_alpha = beta - phi + np.pi
new_beta = beta

new_i = [i[0], i[1], new_theta, new_phi, i[4]]
new_diags[em], new_diags[en] = np.exp(1j * new_alpha), np.exp(1j * new_beta)

new_tlist = new_tlist + [new_i]

return new_tlist, new_diags, None

[docs]def mach_zehnder(m, n, internal_phase, external_phase, nmax):
r"""A two-mode Mach-Zehnder interferometer section.

This section is constructed by an external phase shifter on the input mode
m, a symmetric beamsplitter combining modes m and n, an internal phase
shifter on mode m, and another symmetric beamsplitter combining modes m
and n.
"""
Rexternal = np.identity(nmax, dtype=np.complex128)
Rexternal[m, m] = np.exp(1j * external_phase)
Rinternal = np.identity(nmax, dtype=np.complex128)
Rinternal[m, m] = np.exp(1j * internal_phase)
BS = np.identity(nmax, dtype=np.complex128)
BS[m, m] = 1.0 / np.sqrt(2)
BS[m, n] = 1.0j / np.sqrt(2)
BS[n, m] = 1.0j / np.sqrt(2)
BS[n, n] = 1.0 / np.sqrt(2)
return BS @ Rinternal @ BS @ Rexternal

[docs]def rectangular_symmetric(V, tol=1e-11):
r"""Decomposition of a unitary into an array of symmetric beamsplitters.

This decomposition starts with the output from :func:rectangular_phase_end
and further decomposes each of the T unitaries into Mach-Zehnder
interferometers consisting of two phase-shifters and two symmetric (50:50)
beamsplitters.

The two beamsplitters in this decomposition of T are modeled by :class:~.ops.BSgate
with arguments :math:(\pi/4, \pi/2), and the two phase-shifters (see :class:~.ops.Rgate)
act on the input mode with the lower index of the two. The phase imposed
by the first phaseshifter (before the first beamsplitter) is named
external_phase, while we call the phase shift between the beamsplitters
internal_phase.

The algorithm applied in this function makes use of the following identity:

.. code-block:: python

Rgate(alpha) | 1
Rgate(beta) | 2
Rgate(phi) | 1
BSgate(theta, 0) | 1, 2

equals

Rgate(phi+alpha-beta) | 1
BSgate(pi/4, pi/2) | 1, 2
Rgate(2*theta+pi) | 1, 2
BSgate(pi/4, pi/2) | 1, 2
Rgate(beta-theta+pi) | 1
Rgate(beta-theta) | 2

The phase-shifts by alpha and beta are thus pushed consecutively through
all the T unitaries of the interferometer and these unitaries are converted
into pairs of symmetric beamsplitters with two phase shifts. The phase
shifts at the end of the interferometer are added to the ones from the
diagonal unitary at the end of the interferometer obtained from :func:~.rectangular_phase_end.

Args:
V (array): unitary matrix of size n_size
tol (int): the number of decimal places to use when determining
whether the matrix is unitary

Returns:
tuple[array]: returns a tuple of the form (tlist,np.diag(localV), None)
where:

* tlist: list containing [n, m, internal_phase, external_phase, n_size] of the T unitaries needed
* localV: Diagonal unitary matrix to be applied at the end of circuit
* None: the value None, in order to make the return
signature identical to :func:rectangular
"""
tlist, diags, _ = rectangular_phase_end(V, tol)
new_tlist, new_diags = [], np.ones(len(diags), dtype=diags.dtype)
for i in tlist:
em, en = int(i[0]), int(i[1])
alpha, beta = np.angle(new_diags[em]), np.angle(new_diags[en])
theta, phi = i[2], i[3]
external_phase = (phi + alpha - beta) % (2 * np.pi)
internal_phase = (np.pi + 2.0 * theta) % (2 * np.pi)
# repeat modulo operations , otherwise the input unitary
# numpy.identity(20) yields an external_phase of exactly 2 * pi
external_phase %= 2 * np.pi
internal_phase %= 2 * np.pi
new_alpha = beta - theta + np.pi
new_beta = 0 * np.pi - theta + beta
new_i = [i[0], i[1], internal_phase, external_phase, i[4]]
new_diags[em], new_diags[en] = np.exp(1j * new_alpha), np.exp(1j * new_beta)
new_tlist = new_tlist + [new_i]

new_diags = diags * new_diags

return new_tlist, new_diags, None

[docs]def triangular(V, tol=1e-11):
r"""Triangular decomposition of a unitary matrix due to Reck et al.

See :cite:reck1994 for more details and :cite:clements2016 for details on notation.

Args:
V (array[complex]): unitary matrix of size n_size
tol (float): the tolerance used when checking if the matrix is unitary:
:math:|VV^\dagger-I| \leq tol

Returns:
tuple[array]: returns a tuple of the form (tlist,np.diag(localV), None)
where:

* tlist: list containing [n,m,theta,phi,n_size] of the T unitaries needed
* localV: Diagonal unitary applied at the beginning of circuit
"""
localV = V
(nsize, _) = localV.shape

diffn = np.linalg.norm(V @ V.conj().T - np.identity(nsize))
if diffn >= tol:
raise ValueError("The input matrix is not unitary")

tlist = []
for i in range(nsize - 2, -1, -1):
for j in range(i + 1):
tlist.append(nullT(nsize - j - 1, nsize - i - 2, localV))
localV = T(*tlist[-1]) @ localV

return list(reversed(tlist)), np.diag(localV), None

[docs]def williamson(V, tol=1e-11):
r"""Williamson decomposition of positive-definite (real) symmetric matrix.

See :ref:williamson.

Note that it is assumed that the symplectic form is

.. math:: \Omega = \begin{bmatrix}0&I\\-I&0\end{bmatrix}

where :math:I is the identity matrix and :math:0 is the zero matrix.

See https://math.stackexchange.com/questions/1171842/finding-the-symplectic-matrix-in-williamsons-theorem/2682630#2682630

Args:
V (array[float]): positive definite symmetric (real) matrix
tol (float): the tolerance used when checking if the matrix is symmetric: :math:|V-V^T| \leq tol

Returns:
tuple[array,array]: (Db, S) where Db is a diagonal matrix
and S is a symplectic matrix such that :math:V = S^T Db S
"""
(n, m) = V.shape

if n != m:
raise ValueError("The input matrix is not square")

diffn = np.linalg.norm(V - np.transpose(V))

if diffn >= tol:
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)
rotmat = changebasis(n)
vals = np.linalg.eigvalsh(V)

for val in vals:
if val <= 0:
raise ValueError("Input matrix is not positive definite")

Mm12 = sqrtm(np.linalg.inv(V)).real
r1 = Mm12 @ omega @ Mm12
s1, K = schur(r1)
X = np.array([[0, 1], [1, 0]])
I = np.identity(2)
seq = []

# In what follows I construct a permutation matrix p  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 I use rotmat to
# go to the ordering x_1, ..., x_n, p_1, ... , p_n

for i in range(n):
if s1[2 * i, 2 * i + 1] > 0:
seq.append(I)
else:
seq.append(X)

p = block_diag(*seq)
Kt = K @ p
s1t = p @ s1 @ p
dd = np.transpose(rotmat) @ s1t @ rotmat
Ktt = Kt @ rotmat
Db = np.diag([1 / dd[i, i + n] for i in range(n)] + [1 / dd[i, i + n] for i in range(n)])
S = Mm12 @ Ktt @ sqrtm(Db)
return Db, np.linalg.inv(S).T

[docs]def bloch_messiah(S, tol=1e-10, rounding=9):
r"""Bloch-Messiah decomposition of a symplectic matrix.

See :ref:bloch_messiah.

Decomposes a symplectic matrix into two symplectic unitaries and squeezing transformation.
It automatically sorts the squeezers so that they respect the canonical symplectic form.

Note that it is assumed that the symplectic form is

.. math:: \Omega = \begin{bmatrix}0&I\\-I&0\end{bmatrix}

where :math:I is the identity matrix and :math:0 is the zero matrix.

As in the Takagi decomposition, the singular values of N are considered
equal if they are equal after np.round(values, rounding).

If S is a passive transformation, then return the S as the first passive
transformation, and set the the squeezing and second unitary matrices to
identity. This choice is not unique.

https://math.stackexchange.com/questions/1886038/finding-euler-decomposition-of-a-symplectic-matrix

Args:
S (array[float]): symplectic matrix
tol (float): the tolerance used when checking if the matrix is symplectic:
:math:|S^T\Omega S-\Omega| \leq tol
rounding (int): the number of decimal places to use when rounding the singular values

Returns:
tuple[array]: Returns the tuple (ut1, st1, vt1). ut1 and vt1 are symplectic orthogonal,
and st1 is diagonal and of the form :math:= \text{diag}(s1,\dots,s_n, 1/s_1,\dots,1/s_n)
such that :math:S = ut1  st1  v1
"""
(n, m) = S.shape

if n != m:
raise ValueError("The input matrix is not square")
if n % 2 != 0:
raise ValueError("The input matrix must have an even number of rows/columns")

n = n // 2
omega = sympmat(n)
if np.linalg.norm(np.transpose(S) @ omega @ S - omega) >= tol:
raise ValueError("The input matrix is not symplectic")

if np.linalg.norm(np.transpose(S) @ S - np.eye(2 * n)) >= tol:

u, sigma = polar(S, side="left")
ss, uss = takagi(sigma, tol=tol, rounding=rounding)

# Apply a permutation matrix so that the squeezers appear in the order
# s_1,...,s_n, 1/s_1,...1/s_n
perm = np.array(list(range(0, n)) + list(reversed(range(n, 2 * n))))

pmat = np.identity(2 * n)[perm, :]
ut = uss @ pmat

# Apply a second permutation matrix to permute s
# (and their corresonding inverses) to get the canonical symplectic form
qomega = np.transpose(ut) @ (omega) @ ut
st = pmat @ np.diag(ss) @ pmat

# Identifying degenerate subspaces
result = []
for _k, g in groupby(np.round(np.diag(st), rounding)[:n]):
result.append(list(g))

stop_is = list(np.cumsum([len(res) for res in result]))
start_is = [0] + stop_is[:-1]

# Rotation matrices (not permutations) based on svd.
# See Appendix B2 of Serafini's book for more details.
u_list, v_list = [], []

for start_i, stop_i in zip(start_is, stop_is):
x = qomega[start_i:stop_i, n + start_i : n + stop_i].real
u_svd, _s_svd, v_svd = np.linalg.svd(x)
u_list = u_list + [u_svd]
v_list = v_list + [v_svd.T]

pmat1 = block_diag(*(u_list + v_list))

st1 = pmat1.T @ pmat @ np.diag(ss) @ pmat @ pmat1
ut1 = uss @ pmat @ pmat1
v1 = np.transpose(ut1) @ u

else:
ut1 = S
st1 = np.eye(2 * n)
v1 = np.eye(2 * n)

return ut1.real, st1.real, v1.real

[docs]def covmat_to_hamil(V, tol=1e-10):  # pragma: no cover
r"""Converts a covariance matrix to a Hamiltonian.

Given a covariance matrix V of a Gaussian state :math:\rho in the xp ordering,
finds a positive matrix :math:H such that

.. math:: \rho = \exp(-Q^T H Q/2)/Z

where :math:Q = (x_1,\dots,x_n,p_1,\dots,p_n) are the canonical
operators, and Z is the partition function.

For more details, see https://arxiv.org/abs/1507.01941

Args:
V (array): Gaussian covariance matrix
tol (int): the number of decimal places to use when determining if the matrix is symmetric

Returns:
array: positive definite Hamiltonian matrix
"""
(n, m) = V.shape
if n != m:
raise ValueError("Input matrix must be square")
if np.linalg.norm(V - np.transpose(V)) >= tol:
raise ValueError("The input matrix is not symmetric")

n = n // 2
omega = sympmat(n)

vals = np.linalg.eigvalsh(V)
for val in vals:
if val <= 0:
raise ValueError("Input matrix is not positive definite")

W = 1j * V @ omega
l, v = np.linalg.eig(W)
H = (1j * omega @ (v @ np.diag(np.arctanh(1.0 / l.real)) @ np.linalg.inv(v))).real

return H

[docs]def hamil_to_covmat(H, tol=1e-10):  # pragma: no cover
r"""Converts a Hamiltonian matrix to a covariance matrix.

Given a Hamiltonian matrix of a Gaussian state H, finds the equivalent covariance matrix
V in the xp ordering.

For more details, see https://arxiv.org/abs/1507.01941

Args:
H (array): positive definite Hamiltonian matrix
tol (int): the number of decimal places to use when determining if the Hamiltonian is symmetric

Returns:
array: Gaussian covariance matrix
"""
(n, m) = H.shape
if n != m:
raise ValueError("Input matrix must be square")
if np.linalg.norm(H - np.transpose(H)) >= tol:
raise ValueError("The input matrix is not symmetric")

vals = np.linalg.eigvalsh(H)
for val in vals:
if val <= 0:
raise ValueError("Input matrix is not positive definite")

n = n // 2
omega = sympmat(n)

Wi = 1j * omega @ H
l, v = np.linalg.eig(Wi)
V = (1j * (v @ np.diag(1.0 / np.tanh(l.real)) @ np.linalg.inv(v)) @ omega).real
return V