def vibronic(
t: np.ndarray,
U1: np.ndarray,
r: np.ndarray,
U2: np.ndarray,
alpha: np.ndarray,
n_samples: int,
loss: float = 0.0,
) -> list:
"""Generate samples for computing vibronic spectra. The following gates are applied to input
vacuum states:
1. Two-mode squeezing on all :math:`2N` modes with parameters ``t``
2. Interferometer ``U1`` on the first :math:`N` modes
3. Squeezing on the first :math:`N` modes with parameters ``r``
4. Interferometer ``U2`` on the first :math:`N` modes
5. Displacement on the first :math:`N` modes with parameters ``alpha``
A sample is generated by measuring the number of photons in each of the :math:`2N` modes. In
the special case that all of the two-mode squeezing parameters ``t`` are zero, only :math:`N`
modes are considered, which speeds up calculations.
**Example usage:**
>>> t = np.array([0., 0., 0., 0., 0., 0., 0.])
>>> U1 = np.array(
>>> [[-0.07985219, 0.66041032, -0.19389188, 0.01340832, 0.70312675, -0.1208423, -0.10352726],
>>> [0.19216669, -0.12470466, -0.81320519, 0.52045174, -0.1066017, -0.06300751, -0.00376173],
>>> [0.60838109, 0.0835063, -0.14958816, -0.34291399, 0.06239828, 0.68753918, -0.07955415],
>>> [0.63690134, -0.03047939, 0.46585565, 0.50545897, 0.21194805, -0.20422433, 0.18516987],
>>> [0.34556293, 0.22562207, -0.1999159, -0.50280235, -0.25510781, -0.55793978, 0.40065893],
>>> [-0.03377431, -0.66280536, -0.14740447, -0.25725325, 0.6145946, -0.07128058, 0.29804963],
>>> [-0.24570365, 0.22402764, 0.003273, 0.19204683, -0.05125235, 0.3881131, 0.83623564]])
>>> r = np.array(
>>> [0.09721339, 0.07017918, 0.02083469, -0.05974357, -0.07487845, -0.1119975, -0.1866708])
>>> U2 = np.array(
>>> [[-0.07012006, 0.14489772, 0.17593463, 0.02431155, -0.63151781, 0.61230046, 0.41087368],
>>> [0.5618538, -0.09931968, 0.04562272, 0.02158822, 0.35700706, 0.6614837, -0.326946],
>>> [-0.16560687, -0.7608465, -0.25644606, -0.54317241, -0.12822903, 0.12809274, -0.00597384],
>>> [0.01788782, 0.60430409, -0.19831443, -0.73270964, -0.06393682, 0.03376894, -0.23038293],
>>> [0.78640978, -0.11133936, 0.03160537, -0.09188782, -0.43483738, -0.4018141, 0.09582698],
>>> [-0.13664887, -0.11196486, 0.86353995, -0.19608061, -0.12313513, -0.08639263, -0.40251231],
>>> [-0.12060103, -0.01169781, -0.33937036, 0.34662981, -0.49895371, 0.03257453, -0.70709135]])
>>> alpha = np.array(
>>> [0.15938187, 0.10387399, 1.10301587, -0.26756921, 0.32194572, -0.24317402, 0.0436992])
>>> vibronic(t, U1, S, U2, alpha, 2, 0.0)
[[0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
Args:
t (array): two-mode squeezing parameters
U1 (array): unitary matrix for the first interferometer
r (array): squeezing parameters
U2 (array): unitary matrix for the second interferometer
alpha (array): displacement parameters
n_samples (int): number of samples to be generated
loss (float): loss parameter denoting the fraction of generated photons that are lost
Returns:
list[list[int]]: a list of samples from GBS
"""
if n_samples < 1:
raise ValueError("Number of samples must be at least one")
if not 0 <= loss <= 1:
raise ValueError(
"Loss parameter must take a value between zero and one")
n_modes = len(t)
eng = sf.LocalEngine(backend="gaussian")
if np.any(t != 0):
gbs = sf.Program(n_modes * 2)
else:
gbs = sf.Program(n_modes)
# pylint: disable=expression-not-assigned,pointless-statement
with gbs.context as q:
if np.any(t != 0):
for i in range(n_modes):
sf.ops.S2gate(t[i]) | (q[i], q[i + n_modes])
sf.ops.Interferometer(U1) | q[:n_modes]
for i in range(n_modes):
sf.ops.Sgate(r[i]) | q[i]
sf.ops.Interferometer(U2) | q[:n_modes]
for i in range(n_modes):
sf.ops.Dgate(alpha[i]) | q[i]
if loss:
for _q in q:
sf.ops.LossChannel(1 - loss) | _q
sf.ops.MeasureFock() | q
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=UserWarning,
message="Cannot simulate non-")
s = eng.run(gbs, run_options={"shots": n_samples}).samples
s = np.array(s).tolist() # convert all generated samples to list
if n_samples == 1:
s = [s]
if np.any(t == 0):
s = np.pad(s, ((0, 0), (0, n_modes))).tolist()
return s
def prog():
"""Program fixture."""
return sf.Program(2)
tf.random.set_seed(137)
np.random.seed(137)
# define width and depth of CV quantum neural network
modes = 1
layers = 8
cutoff_dim = 6
# defining desired state (single photon state)
target_state = np.zeros(cutoff_dim)
target_state[1] = 1
target_state = tf.constant(target_state, dtype=tf.complex64)
# initialize engine and program
eng = sf.Engine(backend="tf", backend_options={"cutoff_dim": cutoff_dim})
qnn = sf.Program(modes)
# initialize QNN weights
weights = init_weights(modes, layers)
num_params = np.prod(weights.shape)
# Create array of Strawberry Fields symbolic gate arguments, matching
# the size of the weights Variable.
sf_params = np.arange(num_params).reshape(weights.shape).astype(np.str)
sf_params = np.array([qnn.params(*i) for i in sf_params])
# Construct the symbolic Strawberry Fields program by
# looping and applying layers to the program.
with qnn.context as q:
for k in range(layers):
layer(sf_params[k], q)
def sample_tmsv(
r: list,
t: float,
Ul: np.ndarray,
w: np.ndarray,
n_samples: int,
loss: float = 0.0,
) -> list:
r"""Generate samples for simulating vibrational quantum dynamics with a two-mode squeezed
vacuum input state.
This function generates samples from a GBS device with two-mode squeezed vacuum input states.
Given :math:`N` squeezing parameters and an :math:`N`-dimensional normal-to-local transformation
matrix, a GBS device with :math:`2N` modes is simulated. The :func:`~.TimeEvolution` operator
acts only on the first :math:`N` modes in the device. Samples are generated by measuring the
number of photons in each of the :math:`2N` modes.
**Example usage:**
>>> r = [[0.2, 0.1], [0.8, 0.2]]
>>> t = 10.0
>>> Ul = np.array([[0.707106781, -0.707106781],
... [0.707106781, 0.707106781]])
>>> w = np.array([3914.92, 3787.59])
>>> n_samples = 5
>>> sample_tmsv(r, t, Ul, w, n_samples)
[[0, 0, 0, 0], [0, 0, 0, 0], [0, 1, 0, 1], [0, 1, 0, 1], [0, 2, 0, 2]]
Args:
r (list[list[float]]): list of two-mode squeezing gate parameters given as ``[amplitude, phase]`` for all modes
t (float): time in femtoseconds
Ul (array): normal-to-local transformation matrix
w (array): normal mode frequencies :math:`\omega` in units of :math:`\mbox{cm}^{-1}`
n_samples (int): number of samples to be generated
loss (float): loss parameter denoting the fraction of lost photons
Returns:
list[list[int]]: a list of samples
"""
if np.any(np.iscomplex(Ul)):
raise ValueError(
"The normal mode to local mode transformation matrix must be real")
if n_samples < 1:
raise ValueError("Number of samples must be at least one")
if not len(r) == len(Ul):
raise ValueError(
"Number of squeezing parameters and the number of modes in the normal-to-local"
" transformation matrix must be equal")
N = len(Ul)
eng = sf.LocalEngine(backend="gaussian")
prog = sf.Program(2 * N)
# pylint: disable=expression-not-assigned
with prog.context as q:
for i in range(N):
sf.ops.S2gate(r[i][0], r[i][1]) | (q[i], q[i + N])
sf.ops.Interferometer(Ul.T) | q[:N]
TimeEvolution(w, t) | q[:N]
sf.ops.Interferometer(Ul) | q[:N]
if loss:
for _q in q:
sf.ops.LossChannel(1 - loss) | _q
sf.ops.MeasureFock() | q
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=UserWarning,
message="Cannot simulate non-")
s = eng.run(prog, shots=n_samples).samples
return s.tolist()
def prog(backend):
"""Program fixture."""
prog = sf.Program(2)
with prog.context as q:
ops.Dgate(0.5) | q[0]
return prog
def sample(A: np.ndarray,
n_mean: float,
n_samples: int = 1,
threshold: bool = True,
loss: float = 0.0) -> list:
r"""Generate simulated samples from GBS encoded with a symmetric matrix :math:`A`.
**Example usage:**
>>> g = nx.erdos_renyi_graph(5, 0.7)
>>> a = nx.to_numpy_array(g)
>>> sample(a, 3, 4)
[[1, 1, 1, 1, 1], [1, 1, 0, 1, 1], [0, 0, 0, 0, 0], [1, 0, 0, 0, 1]]
Args:
A (array): the symmetric matrix to sample from
n_mean (float): mean photon number
n_samples (int): number of samples
threshold (bool): perform GBS with threshold detectors if ``True`` or photon-number
resolving detectors if ``False``
loss (float): fraction of generated photons that are lost while passing through device.
Parameter should range from ``loss=0`` (ideal noise-free GBS) to ``loss=1``.
Returns:
list[list[int]]: a list of samples from GBS with respect to the input symmetric matrix
"""
if not np.allclose(A, A.T):
raise ValueError(
"Input must be a NumPy array corresponding to a symmetric matrix")
if n_samples < 1:
raise ValueError("Number of samples must be at least one")
if n_mean < 0:
raise ValueError("Mean photon number must be non-negative")
if not 0 <= loss <= 1:
raise ValueError(
"Loss parameter must take a value between zero and one")
nodes = len(A)
p = sf.Program(nodes)
eng = sf.LocalEngine(backend="gaussian")
mean_photon_per_mode = n_mean / float(nodes)
# pylint: disable=expression-not-assigned,pointless-statement
with p.context as q:
sf.ops.GraphEmbed(A, mean_photon_per_mode=mean_photon_per_mode) | q
if loss:
for _q in q:
sf.ops.LossChannel(1 - loss) | _q
if threshold:
sf.ops.MeasureThreshold() | q
else:
sf.ops.MeasureFock() | q
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=UserWarning,
message="Cannot simulate non-")
s = eng.run(p, run_options={"shots": n_samples}).samples
if n_samples == 1:
return [s]
return s.tolist()
def test_no_unitary(self, tol):
"""Test compilation works with no unitary provided"""
prog = sf.Program(8)
with prog.context as q:
ops.S2gate(SQ_AMPLITUDE) | (q[0], q[4])
ops.S2gate(SQ_AMPLITUDE) | (q[1], q[5])
ops.S2gate(SQ_AMPLITUDE) | (q[2], q[6])
ops.S2gate(SQ_AMPLITUDE) | (q[3], q[7])
ops.MeasureFock() | q
res = prog.compile("Xunitary")
expected = sf.Program(8)
with expected.context as q:
ops.S2gate(SQ_AMPLITUDE, 0) | (q[0], q[4])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[1], q[5])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[2], q[6])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[3], q[7])
# corresponds to an identity on modes [0, 1, 2, 3]
# This can be easily seen from below by noting that:
# MZ(pi, pi) = R(0) = I
# MZ(pi, 0) @ MZ(pi, 0) = I
# [R(pi) \otimes I] @ MZ(pi, 0) = I
ops.MZgate(np.pi, 0) | (q[0], q[1])
ops.MZgate(np.pi, 0) | (q[2], q[3])
ops.MZgate(np.pi, np.pi) | (q[1], q[2])
ops.MZgate(np.pi, np.pi) | (q[0], q[1])
ops.MZgate(np.pi, 0) | (q[2], q[3])
ops.MZgate(np.pi, np.pi) | (q[1], q[2])
ops.Rgate(np.pi) | (q[0])
ops.Rgate(0) | (q[1])
ops.Rgate(0) | (q[2])
ops.Rgate(0) | (q[3])
# corresponds to an identity on modes [4, 5, 6, 7]
ops.MZgate(np.pi, 0) | (q[4], q[5])
ops.MZgate(np.pi, 0) | (q[6], q[7])
ops.MZgate(np.pi, np.pi) | (q[5], q[6])
ops.MZgate(np.pi, np.pi) | (q[4], q[5])
ops.MZgate(np.pi, 0) | (q[6], q[7])
ops.MZgate(np.pi, np.pi) | (q[5], q[6])
ops.Rgate(np.pi) | (q[4])
ops.Rgate(0) | (q[5])
ops.Rgate(0) | (q[6])
ops.Rgate(0) | (q[7])
ops.MeasureFock() | q
assert program_equivalence(res, expected, atol=tol, compare_params=False)
# double check that the applied symplectic is correct
# remove the Fock measurements
res.circuit = res.circuit[:-1]
# extract the Gaussian symplectic matrix
O = res.compile("gaussian_unitary").circuit[0].op.p[0]
# construct the expected symplectic matrix corresponding
# to just the initial two mode squeeze gates
S = two_mode_squeezing(SQ_AMPLITUDE, 0)
num_modes = 8
expected = np.identity(2 * num_modes)
for i in range(num_modes // 2):
expected = expand(S, [i, i + num_modes // 2], num_modes) @ expected
# Note that the comparison has to be made at the level of covariance matrices
# Not at the level of symplectic matrices
assert np.allclose(O @ O.T, expected @ expected.T, atol=tol)
#!/usr/bin/env python3
import strawberryfields as sf
from strawberryfields.ops import *
# initialize engine and program objects
eng = sf.Engine(backend="gaussian")
gate_teleportation = sf.Program(4)
with gate_teleportation.context as q:
# create initial states
Squeezed(0.1) | q[0]
Squeezed(-2) | q[1]
Squeezed(-2) | q[2]
# apply the gate to be teleported
Pgate(0.5) | q[1]
# conditional phase entanglement
CZgate(1) | (q[0], q[1])
CZgate(1) | (q[1], q[2])
# projective measurement onto
# the position quadrature
Fourier.H | q[0]
MeasureX | q[0]
Fourier.H | q[1]
MeasureX | q[1]
# compare against the expected output
# X(q1/sqrt(2)).F.P(0.5).X(q0/sqrt(0.5)).F.|z>
# not including the corrections
Squeezed(0.1) | q[3]
# y0ka1
# Made on July 14th, 2021
''' This is a script to run test-circuits to help me learn quantum computing, it will not have a certian structured circuit,
rather I will be testing many circuits so this will be constantly running while I run the code from a seperate terminal. '''
import qiskit
import strawberryfields as sf
import pennylane
from strawberryfields import ops
# Creating a 3-mode quantum program
prog = sf.Program(3)
with prog.context as q:
ops.Sgate(0.54) | q[0]
ops.Sgate(0.54) | q[1]
ops.Sgate(0.54) | q[2]
ops.BSgate(0.43, 0.1) | (q[0], q[2])
ops.BSgate(0.43, 0.1) | (q[1], q[2])
ops.MeasureFock() | q
def test_decomposition(self, tol):
"""Test that a graph is correctly decomposed"""
n = 3
prog = sf.Program(2*n)
A = np.zeros([2*n, 2*n])
B = np.random.random([n, n])
A[:n, n:] = B
A += A.T
sq, U, V = dec.bipartite_graph_embed(B)
G = ops.BipartiteGraphEmbed(A)
cmds = G.decompose(prog.register)
S = np.identity(4 * n)
# calculating the resulting decomposed symplectic
for cmd in cmds:
# all operations should be BSgates, Rgates, or S2gates
assert isinstance(
cmd.op, (ops.Interferometer, ops.S2gate)
)
# build up the symplectic transform
modes = [i.ind for i in cmd.reg]
if isinstance(cmd.op, ops.S2gate):
# check that the registers are i, i+n
assert len(modes) == 2
assert modes[1] == modes[0] + n
r, phi = par_evaluate(cmd.op.p)
assert -r in sq
assert phi == 0
S = _two_mode_squeezing(r, phi, modes, 2*n) @ S
if isinstance(cmd.op, ops.Interferometer):
# check that each unitary only applies to half the modes
assert len(modes) == n
assert modes in ([0, 1, 2], [3, 4, 5])
# check matrix is unitary
U1 = par_evaluate(cmd.op.p[0])
assert np.allclose(U1 @ U1.conj().T, np.identity(n), atol=tol, rtol=0)
if modes[0] == 0:
assert np.allclose(U1, U, atol=tol, rtol=0)
else:
assert modes[0] == 3
assert np.allclose(U1, V, atol=tol, rtol=0)
S_U = np.vstack(
[np.hstack([U1.real, -U1.imag]), np.hstack([U1.imag, U1.real])]
)
S = expand(S_U, modes, 2*n) @ S
# the resulting covariance state
cov = S @ S.T
A_res = Amat(cov)[:2*n, :2*n]
# The bottom right corner of A_res should be identical to A,
# up to some constant scaling factor. Check if the ratio
# of all elements is one
ratio = np.real_if_close(A_res[n:, :n] / B.T)
ratio /= ratio[0, 0]
assert np.allclose(ratio, np.ones([n, n]), atol=tol, rtol=0)
import strawberryfields as sf
from strawberryfields.ops import *
import tensorflow as tf
# Define the variational circuit and its output.
X = tf.placeholder(tf.float32, shape=[2])
y = tf.placeholder(tf.float32, shape=[2])
sdev = 0.05
depth = 50
phi = tf.Variable(tf.random_normal(shape=[depth], stddev=sdev))
# eng, q = sf.Engine(3)
eng = sf.Engine(backend="tf", backend_options={"cutoff_dim": 10})
circuit = sf.Program(2)
with circuit.context as q: #with eng:
# Note that we are feeding 1-d tensors into gates, not scalars!
Dgate(X[0], 0.) | q[0]
Dgate(X[1], 0.) | q[1]
# Dgate(X[0], X[1]) | q[2]
Dgate(phi[0], phi[1]) | q[0]
Dgate(phi[2], phi[3]) | q[1]
# Dgate(phi[4], phi[5]) | q[2]
Sgate(phi[6], phi[7]) | q[0]
Sgate(phi[8], phi[9]) | q[1]
# Sgate(phi[10], phi[11]) | q[2]
Kgate(phi[30]) | q[0]
Kgate(phi[31]) | q[1]
# Kgate(phi[32]) | q[2]
BSgate(phi[12]) | (q[0], q[1])
BSgate() | (q[0], q[1])
def test_gradients2to1():
ket_in = np.zeros((5, 5, 5, 5), dtype=np.complex128)
ket_in[1, 2, 1, 0] = np.sqrt(1 / 3)
ket_in[1, 0, 0, 3] = 1j * np.sqrt(2 / 3)
test_prog = sf.Program(4) # 4 modes
with test_prog.context as q:
sf.ops.Ket(ket_in) | (q[0], q[1], q[2], q[3])
sf.ops.BSgate(0.1804, 0.0578) | (q[0], q[1])
sf.ops.BSgate(0.06406, 1.5165) | (q[2], q[3])
sf.ops.BSgate(1.473, 0.1176) | (q[1], q[2])
sf.ops.BSgate(0.263, -0.2517) | (q[0], q[1])
sf.ops.BSgate(0.3323, 1.9946) | (q[2], q[3])
sf.ops.BSgate(0.311, -0.3231) | (q[1], q[2])
sf.ops.BSgate(0.4348, 0.0798) | (q[0], q[1])
sf.ops.BSgate(0.0368, 0.6157) | (q[2], q[3])
# eng = sf.Engine(backend="fock", backend_options={"cutoff_dim": 5})
# ket_out_1 = eng.run(test_prog).state.ket()
prog_unitary = sf.Program(4)
prog_unitary.circuit = test_prog.circuit[1:]
prog_compiled = prog_unitary.compile(compiler="gaussian_unitary")
S = prog_compiled.circuit[0].op.p[0]
U1 = S[:4, :4] + 1j * S[4:, :4]
ket_in = np.zeros((5, 5, 5, 5), dtype=np.complex128)
ket_in[1, 2, 1, 0] = np.sqrt(1 / 3)
ket_in[1, 0, 0, 3] = 1j * np.sqrt(2 / 3)
test_prog = sf.Program(4) # 4 modes
with test_prog.context as q:
sf.ops.Ket(ket_in) | (q[0], q[1], q[2], q[3])
sf.ops.BSgate(0.1804, 0.0568) | (q[0], q[1])
sf.ops.BSgate(0.06306, 1.5165) | (q[2], q[3])
sf.ops.BSgate(1.462, 0.1176) | (q[1], q[2])
sf.ops.BSgate(0.263, -0.2507) | (q[0], q[1])
sf.ops.BSgate(0.3333, 1.9946) | (q[2], q[3])
sf.ops.BSgate(0.312, -0.3231) | (q[1], q[2])
sf.ops.BSgate(0.4358, 0.0788) | (q[0], q[1])
sf.ops.BSgate(0.0358, 0.6157) | (q[2], q[3])
# eng = sf.Engine(backend="fock", backend_options={"cutoff_dim": 5})
# ket_out_2 = eng.run(test_prog).state.ket()
prog_unitary = sf.Program(4)
prog_unitary.circuit = test_prog.circuit[1:]
prog_compiled = prog_unitary.compile(compiler="gaussian_unitary")
S = prog_compiled.circuit[0].op.p[0]
U2 = S[:4, :4] + 1j * S[4:, :4]
dU = U2 - U1
s_in = State({
(1, 2, 1, 0): np.sqrt(1 / 3),
(1, 0, 0, 3): 1j * np.sqrt(2 / 3)
})
s_out = State({(4, 0, 0, 0): 1.0})
io = IOSpec(input_state=s_in, output_state=s_out)
tree1 = Tree(io=io, covariance_matrix=U1)
tree2 = Tree(io=io, covariance_matrix=U1 + dU)
amp1, grad1 = tree1.amplitude()
amp2, grad2 = tree2.amplitude()
print(amp2, amp1 + np.sum(grad1 * dU))
print(np.sum(grad1 * dU), amp2 - amp1)
assert np.isclose(amp2, amp1 + np.sum(grad1 * dU))
assert np.isclose(amp1, amp2 - np.sum(grad2 * dU))
import strawberryfields as sf
from strawberryfields.ops import *
prog = sf.Program(2)
import tensorflow as tf
input_ = tf.placeholder(tf.float32, shape=(2, 1))
weights = tf.Variable([[0.1, 0.1]])
bias = tf.Variable(0.0)
NN = tf.sigmoid(tf.matmul(weights, input_) + bias)
NNDgate = Dgate(NN)
@sf.convert
def sigmoid(x):
return tf.sigmoid(x)
prog = sf.Program(2)
with prog.context as q:
MeasureX | q[0]
Dgate(sigmoid(q[0])) | q[1]
batch_size = 3
prog = sf.Program(2)
eng = sf.Engine('tf',
backend_options={
"cutoff_dim": 7,
"batch_size": batch_size
tf.clip_by_value(disp_magnitude_variables[layer_number, i],
-disp_clip, disp_clip),
disp_phase_variables[layer_number, i]) | q[i]
for i in range(mode_number):
Kgate(
tf.clip_by_value(kerr_variables[layer_number, i], -kerr_clip,
kerr_clip)) | q[i]
# ===================================================================================
# Defining QNN
# ===================================================================================
# construct the two-mode Strawberry Fields program
prog = sf.Program(mode_number)
# construct the circuit
with prog.context as q:
input_qnn_layer()
for i in range(depth):
qnn_layer(i)
# create an engine
eng = sf.Engine('tf',
backend_options={
"cutoff_dim": cutoff,
"batch_size": batch_size
})
def vibronic(
t: np.ndarray,
U1: np.ndarray,
r: np.ndarray,
U2: np.ndarray,
alpha: np.ndarray,
n_samples: int,
loss: float = 0.0,
) -> list:
"""Generate samples for computing vibronic spectra. The following gates are applied to input
vacuum states:
1. Two-mode squeezing on all :math:`2N` modes with parameters ``t``
2. Interferometer ``U1`` on the first :math:`N` modes
3. Squeezing on the first :math:`N` modes with parameters ``r``
4. Interferometer ``U2`` on the first :math:`N` modes
5. Displacement on the first :math:`N` modes with parameters ``alpha``
A sample is generated by measuring the number of photons in each of the :math:`2N` modes. In
the special case that all of the two-mode squeezing parameters ``t`` are zero, only :math:`N`
modes are considered, which speeds up calculations.
**Example usage:**
>>> formic = data.Formic()
>>> w = formic.w
>>> wp = formic.wp
>>> Ud = formic.Ud
>>> delta = formic.delta
>>> T = 0
>>> t, U1, r, U2, alpha = vibronic.gbs_params(w, wp, Ud, delta, T)
>>> sample.vibronic(t, U1, r, U2, alpha, 2, 0.0)
[[0, 0, 2, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0],
[0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]
Args:
t (array): two-mode squeezing parameters
U1 (array): unitary matrix for the first interferometer
r (array): squeezing parameters
U2 (array): unitary matrix for the second interferometer
alpha (array): displacement parameters
n_samples (int): number of samples to be generated
loss (float): loss parameter denoting the fraction of generated photons that are lost
Returns:
list[list[int]]: a list of samples from GBS
"""
if n_samples < 1:
raise ValueError("Number of samples must be at least one")
if not 0 <= loss <= 1:
raise ValueError(
"Loss parameter must take a value between zero and one")
n_modes = len(t)
eng = sf.LocalEngine(backend="gaussian")
if np.any(t != 0):
gbs = sf.Program(n_modes * 2)
else:
gbs = sf.Program(n_modes)
# pylint: disable=expression-not-assigned,pointless-statement
with gbs.context as q:
if np.any(t != 0):
for i in range(n_modes):
sf.ops.S2gate(t[i]) | (q[i], q[i + n_modes])
sf.ops.Interferometer(U1) | q[:n_modes]
for i in range(n_modes):
sf.ops.Sgate(r[i]) | q[i]
sf.ops.Interferometer(U2) | q[:n_modes]
for i in range(n_modes):
sf.ops.Dgate(alpha[i]) | q[i]
if loss:
for _q in q:
sf.ops.LossChannel(1 - loss) | _q
sf.ops.MeasureFock() | q
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=UserWarning,
message="Cannot simulate non-")
s = eng.run(gbs, run_options={"shots": n_samples}).samples
s = np.array(s).tolist() # convert all generated samples to list
if n_samples == 1:
s = [s]
if np.any(t == 0):
s = np.pad(s, ((0, 0), (0, n_modes))).tolist()
return s
#!/usr/bin/env python3
import strawberryfields as sf
from strawberryfields.ops import *
from numpy import pi
# initialize engine and program objects
eng = sf.Engine(backend="fock", backend_options={"cutoff_dim": 5})
ham_simulation = sf.Program(2)
# set the Hamiltonian parameters
J = 1 # hopping transition
U = 1.5 # on-site interaction
k = 20 # Lie product decomposition terms
t = 1.086 # timestep
theta = -J * t / k
r = -U * t / (2 * k)
with ham_simulation.context as q:
# prepare the initial state
Fock(2) | q[0]
# Two node tight-binding
# Hamiltonian simulation
for i in range(k):
BSgate(theta, pi / 2) | (q[0], q[1])
Kgate(r) | q[0]
Rgate(-r) | q[0]
Kgate(r) | q[1]
Rgate(-r) | q[1]
# end circuit
def prog(self):
"""Dummy program context for each test"""
prog = sf.Program(2)
Program._current_context = prog
yield prog
Program._current_context = None
#!/usr/bin/env python3
import strawberryfields as sf
from strawberryfields.ops import *
from strawberryfields.utils import scale
from numpy import pi, sqrt
# initialize engine and program objects
eng = sf.Engine(backend="gaussian")
teleportation = sf.Program(3)
with teleportation.context as q:
psi, alice, bob = q[0], q[1], q[2]
# state to be teleported:
Coherent(1+0.5j) | psi
# 50-50 beamsplitter
BS = BSgate(pi/4, 0)
# maximally entangled states
Squeezed(-2) | alice
Squeezed(2) | bob
BS | (alice, bob)
# Alice performs the joint measurement
# in the maximally entangled basis
BS | (psi, alice)
MeasureX | psi
MeasureP | alice
# Bob conditionally displaces his mode
def gaussian(
U1: np.ndarray,
r: np.ndarray,
U2: np.ndarray,
alpha: np.ndarray,
n_samples: int,
loss: float = 0.0,
) -> list:
r"""Generate simulated samples from pure Gaussian states.
The pure Gaussian states are prepared by applying the following gates to the vacuum:
#. Interferometer ``U1``
#. Squeezing on all modes with parameters ``r``
#. Interferometer ``U2``
#. Displacement on all modes with parameters ``alpha``
Note that, since the gates are applied to the vacuum, the first interferometer has no effect.
It is nevertheless included so that the inputs to this function follow a standard decomposition
of Gaussian unitaries shown :ref:`above <decomposition>`.
Args:
U1 (array): first interferometer unitary matrix
r (array): squeezing parameters
U2 (array): second interferometer unitary matrix
alpha (array): displacement parameters
n_samples (int): number of samples to be generated
loss (float): fraction of generated photons that are lost while passing through device.
Parameter should range from ``loss=0`` (ideal noise-free GBS) to ``loss=1``.
Returns:
list[list[int]]: a list of samples from GBS
"""
if n_samples < 1:
raise ValueError("Number of samples must be at least one")
if not 0 <= loss <= 1:
raise ValueError(
"Loss parameter must take a value between zero and one")
n_modes = len(alpha)
eng = sf.LocalEngine(backend="gaussian")
gbs = sf.Program(n_modes)
# pylint: disable=expression-not-assigned,pointless-statement
with gbs.context as q:
# this interferometer has no action, but is kept to follow the decomposition given in the
# docstring
sf.ops.Interferometer(U1) | q
for i in range(n_modes):
sf.ops.Sgate(r[i]) | q[i]
sf.ops.Interferometer(U2) | q
for i in range(n_modes):
sf.ops.Dgate(alpha[i]) | q[i]
if loss:
for _q in q:
sf.ops.LossChannel(1 - loss) | _q
sf.ops.MeasureFock() | q
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=UserWarning,
message="Cannot simulate non-")
s = eng.run(gbs, run_options={"shots": n_samples}).samples
if n_samples == 1:
return [s]
return s.tolist()
#!/usr/bin/env python3
import numpy as np
import strawberryfields as sf
from strawberryfields.ops import *
# initialize engine and program objects
eng = sf.Engine(backend="gaussian")
gbs = sf.Program(4)
# define the linear interferometer
U = np.array(
[[
0.219546940711 - 0.256534554457j, 0.611076853957 + 0.524178937791j,
-0.102700187435 + 0.474478834685j, -0.027250232925 + 0.03729094623j
],
[
0.451281863394 + 0.602582912475j, 0.456952590016 + 0.01230749109j,
0.131625867435 - 0.450417744715j, 0.035283194078 - 0.053244267184j
],
[
0.038710094355 + 0.492715562066j, -0.019212744068 - 0.321842852355j,
-0.240776471286 + 0.524432833034j, -0.458388143039 + 0.329633367819j
],
[
-0.156619083736 + 0.224568570065j, 0.109992223305 - 0.163750223027j,
-0.421179844245 + 0.183644837982j, 0.818769184612 + 0.068015658737j
]])
with gbs.context as q:
# prepare the input squeezed states
S = Sgate(1)
def sample_fock(
input_state: list,
t: float,
Ul: np.ndarray,
w: np.ndarray,
n_samples: int,
cutoff: int,
loss: float = 0.0,
) -> list:
r"""Generate samples for simulating vibrational quantum dynamics with an input Fock state.
**Example usage:**
>>> input_state = [0, 2]
>>> t = 10.0
>>> Ul = np.array([[0.707106781, -0.707106781],
... [0.707106781, 0.707106781]])
>>> w = np.array([3914.92, 3787.59])
>>> n_samples = 5
>>> cutoff = 5
>>> sample_fock(input_state, t, Ul, w, n_samples, cutoff)
[[0, 2], [0, 2], [1, 1], [0, 2], [0, 2]]
Args:
input_state (list): input Fock state
t (float): time in femtoseconds
Ul (array): normal-to-local transformation matrix
w (array): normal mode frequencies :math:`\omega` in units of :math:`\mbox{cm}^{-1}`
n_samples (int): number of samples to be generated
cutoff (int): cutoff dimension for each mode
loss (float): loss parameter denoting the fraction of lost photons
Returns:
list[list[int]]: a list of samples
"""
if np.any(np.iscomplex(Ul)):
raise ValueError(
"The normal mode to local mode transformation matrix must be real")
if n_samples < 1:
raise ValueError("Number of samples must be at least one")
if not len(input_state) == len(Ul):
raise ValueError(
"Number of modes in the input state and the normal-to-local transformation"
" matrix must be equal")
if np.any(np.array(input_state) < 0):
raise ValueError("Input state must not contain negative values")
if max(input_state) >= cutoff:
raise ValueError(
"Number of photons in each input mode must be smaller than cutoff")
modes = len(Ul)
s = []
eng = sf.Engine("fock", backend_options={"cutoff_dim": cutoff})
prog = sf.Program(modes)
# pylint: disable=expression-not-assigned
with prog.context as q:
for i in range(modes):
sf.ops.Fock(input_state[i]) | q[i]
sf.ops.Interferometer(Ul.T) | q
TimeEvolution(w, t) | q
sf.ops.Interferometer(Ul) | q
if loss:
for _q in q:
sf.ops.LossChannel(1 - loss) | _q
sf.ops.MeasureFock() | q
for _ in range(n_samples):
s.append(eng.run(prog).samples[0].tolist())
return s
def test_decomposition(self, hbar, tol):
"""Test that an graph is correctly decomposed"""
n = 3
prog = sf.Program(n)
A = np.random.random([n, n]) + 1j * np.random.random([n, n])
A += A.T
A -= np.trace(A) * np.identity(n) / n
sq, U = dec.graph_embed(A)
G = ops.GraphEmbed(A)
cmds = G.decompose(prog.register)
assert np.all(sq == G.sq)
assert np.all(U == G.U)
S = np.identity(2 * n)
# calculating the resulting decomposed symplectic
for cmd in cmds:
# all operations should be BSgates, Rgates, or Sgates
assert isinstance(
cmd.op, (ops.Interferometer, ops.BSgate, ops.Rgate, ops.Sgate))
# build up the symplectic transform
modes = [i.ind for i in cmd.reg]
if isinstance(cmd.op, ops.Sgate):
S = _squeezing(cmd.op.p[0].x, cmd.op.p[1].x, modes, n) @ S
if isinstance(cmd.op, ops.Rgate):
S = _rotation(cmd.op.p[0].x, modes, n) @ S
if isinstance(cmd.op, ops.BSgate):
S = _beamsplitter(cmd.op.p[0].x, cmd.op.p[1].x, modes, n) @ S
if isinstance(cmd.op, ops.Interferometer):
U1 = cmd.op.p[0].x
S_U = np.vstack([
np.hstack([U1.real, -U1.imag]),
np.hstack([U1.imag, U1.real])
])
S = S_U @ S
# the resulting covariance state
cov = S @ S.T
# calculate Hamilton's A matrix: A = X.(I-Q^{-1})*
I = np.identity(n)
O = np.zeros_like(I)
X = np.block([[O, I], [I, O]])
x = cov[:n, :n]
xp = cov[:n, n:]
p = cov[n:, n:]
aidaj = (x + p + 1j * (xp - xp.T) - 2 * I) / 4
aiaj = (x - p + 1j * (xp + xp.T)) / 4
Q = np.block([[aidaj, aiaj.conj()], [aiaj, aidaj.conj()]
]) + np.identity(2 * n)
A_res = X @ (np.identity(2 * n) - np.linalg.inv(Q)).conj()
# The bottom right corner of A_res should be identical to A,
# up to some constant scaling factor. Check if the ratio
# of all elements is one
ratio = np.real_if_close(A_res[n:, n:] / A)
ratio /= ratio[0, 0]
assert np.allclose(ratio, np.ones([n, n]), atol=tol, rtol=0)
def sample_coherent(
alpha: list,
t: float,
Ul: np.ndarray,
w: np.ndarray,
n_samples: int,
loss: float = 0.0,
) -> list:
r"""Generate samples for simulating vibrational quantum dynamics with an input coherent state.
**Example usage:**
>>> alpha = [[0.3, 0.5], [1.4, 0.1]]
>>> t = 10.0
>>> Ul = np.array([[0.707106781, -0.707106781],
... [0.707106781, 0.707106781]])
>>> w = np.array([3914.92, 3787.59])
>>> n_samples = 5
>>> sample_coherent(alpha, t, Ul, w, n_samples)
[[0, 2], [0, 1], [0, 3], [0, 2], [0, 1]]
Args:
alpha (list[list[float]]): list of displacement parameters given as ``[magnitudes, angles]``
for all modes
t (float): time in femtoseconds
Ul (array): normal-to-local transformation matrix
w (array): normal mode frequencies :math:`\omega` in units of :math:`\mbox{cm}^{-1}`
n_samples (int): number of samples to be generated
loss (float): loss parameter denoting the fraction of lost photons
Returns:
list[list[int]]: a list of samples
"""
if np.any(np.iscomplex(Ul)):
raise ValueError(
"The normal mode to local mode transformation matrix must be real")
if n_samples < 1:
raise ValueError("Number of samples must be at least one")
if not len(alpha) == len(Ul):
raise ValueError(
"Number of displacement parameters and the number of modes in the normal-to-local"
" transformation matrix must be equal")
modes = len(Ul)
eng = sf.LocalEngine(backend="gaussian")
prog = sf.Program(modes)
# pylint: disable=expression-not-assigned
with prog.context as q:
for i in range(modes):
sf.ops.Dgate(alpha[i][0], alpha[i][1]) | q[i]
sf.ops.Interferometer(Ul.T) | q
TimeEvolution(w, t) | q
sf.ops.Interferometer(Ul) | q
if loss:
for _q in q:
sf.ops.LossChannel(1 - loss) | _q
sf.ops.MeasureFock() | q
with warnings.catch_warnings():
warnings.filterwarnings("ignore",
category=UserWarning,
message="Cannot simulate non-")
s = eng.run(prog, shots=n_samples).samples
return s.tolist()
def test_no_unitary(self, chip, tol):
"""Test compilation works with no unitary provided"""
prog = sf.Program(12)
with prog.context as q:
ops.S2gate(SQ_AMPLITUDE) | (q[0], q[6])
ops.S2gate(SQ_AMPLITUDE) | (q[1], q[7])
ops.S2gate(SQ_AMPLITUDE) | (q[2], q[8])
ops.S2gate(SQ_AMPLITUDE) | (q[3], q[9])
ops.S2gate(SQ_AMPLITUDE) | (q[4], q[10])
ops.S2gate(SQ_AMPLITUDE) | (q[5], q[11])
ops.MeasureFock() | q
res = prog.compile(chip.short_name)
expected = sf.Program(12)
with expected.context as q:
ops.S2gate(SQ_AMPLITUDE, 0) | (q[0], q[6])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[1], q[7])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[2], q[8])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[3], q[9])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[4], q[10])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[5], q[11])
# corresponds to an identity on modes [0, 1, 2, 3, 4, 5]
# This can be easily seen from below by noting that:
# MZ(pi, pi) = R(0) = I
# MZ(pi, 0) @ MZ(pi, 0) = I
# [R(pi) \otimes I] @ MZ(pi, 0) = I
ops.MZgate(np.pi, 0) | (q[0], q[1])
ops.MZgate(np.pi, 0) | (q[2], q[3])
ops.MZgate(np.pi, 0) | (q[4], q[5])
ops.MZgate(np.pi, np.pi) | (q[1], q[2])
ops.MZgate(np.pi, np.pi) | (q[3], q[4])
ops.MZgate(np.pi, 0) | (q[0], q[1])
ops.MZgate(np.pi, 0) | (q[2], q[3])
ops.MZgate(np.pi, 0) | (q[4], q[5])
ops.MZgate(np.pi, np.pi) | (q[1], q[2])
ops.MZgate(np.pi, np.pi) | (q[3], q[4])
ops.MZgate(np.pi, 0) | (q[0], q[1])
ops.MZgate(np.pi, np.pi) | (q[2], q[3])
ops.MZgate(np.pi, np.pi) | (q[4], q[5])
ops.MZgate(np.pi, np.pi) | (q[1], q[2])
ops.MZgate(np.pi, np.pi) | (q[3], q[4])
ops.Rgate(np.pi) | (q[0])
ops.Rgate(0) | (q[1])
ops.Rgate(0) | (q[2])
ops.Rgate(0) | (q[3])
ops.Rgate(0) | (q[4])
ops.Rgate(0) | (q[5])
# corresponds to an identity on modes [6, 7, 8, 9, 10, 11]
ops.MZgate(np.pi, 0) | (q[6], q[7])
ops.MZgate(np.pi, 0) | (q[8], q[9])
ops.MZgate(np.pi, 0) | (q[10], q[11])
ops.MZgate(np.pi, np.pi) | (q[7], q[8])
ops.MZgate(np.pi, np.pi) | (q[9], q[10])
ops.MZgate(np.pi, 0) | (q[6], q[7])
ops.MZgate(np.pi, 0) | (q[8], q[9])
ops.MZgate(np.pi, 0) | (q[10], q[11])
ops.MZgate(np.pi, np.pi) | (q[7], q[8])
ops.MZgate(np.pi, np.pi) | (q[9], q[10])
ops.MZgate(np.pi, 0) | (q[6], q[7])
ops.MZgate(np.pi, np.pi) | (q[8], q[9])
ops.MZgate(np.pi, np.pi) | (q[10], q[11])
ops.MZgate(np.pi, np.pi) | (q[7], q[8])
ops.MZgate(np.pi, np.pi) | (q[9], q[10])
ops.Rgate(np.pi) | (q[6])
ops.Rgate(0) | (q[7])
ops.Rgate(0) | (q[8])
ops.Rgate(0) | (q[9])
ops.Rgate(0) | (q[10])
ops.Rgate(0) | (q[11])
ops.MeasureFock() | q
# Check that the applied symplectic is correct
# remove the Fock measurements
res.circuit = res.circuit[:-1]
# extract the Gaussian symplectic matrix
O = res.compile("gaussian_unitary").circuit[0].op.p[0]
# construct the expected symplectic matrix corresponding
# to just the initial two mode squeeze gates
S = TMS(SQ_AMPLITUDE, 0)
expected = np.zeros([2*12, 2*12])
l = 12 // 2
ch = np.cosh(SQ_AMPLITUDE) * np.identity(l)
sh = np.sinh(SQ_AMPLITUDE) * np.identity(l)
zh = np.zeros([l, l])
expected = np.block([[ch, sh, zh, zh], [sh, ch, zh, zh], [zh, zh, ch, -sh], [zh, zh, -sh, ch]])
assert np.allclose(O, expected, atol=tol)
def execution_context(self):
"""Initialize the engine"""
self.reset()
self.prog = sf.Program(self.num_wires)
self.q = self.prog.register
return self.prog
#!/usr/bin/env python3
import strawberryfields as sf
from strawberryfields.ops import *
from strawberryfields.utils import scale
from numpy import pi, sqrt
import numpy as np
# initialize engine and program objects
eng = sf.Engine(backend="gaussian")
gaussian_cloning = sf.Program(4)
with gaussian_cloning.context as q:
# state to be cloned
Coherent(0.7+1.2j) | q[0]
# 50-50 beamsplitter
BS = BSgate(pi/4, 0)
# symmetric Gaussian cloning scheme
BS | (q[0], q[1])
BS | (q[1], q[2])
MeasureX | q[1]
MeasureP | q[2]
Xgate(scale(q[1], sqrt(2))) | q[0]
Zgate(scale(q[2], sqrt(2))) | q[0]
# after the final beamsplitter, modes q[0] and q[3]
# will contain identical approximate clones of the
# initial state Coherent(0.1+0j)
BS | (q[0], q[3])
# end circuit
params = [r1, sq_r, sq_phi, r2, d_r, d_phi, kappa]
# layer architecture
def layer(i, q):
Rgate(r1[i]) | q
Sgate(sq_r[i], sq_phi[i]) | q
Rgate(r2[i]) | q
Dgate(d_r[i], d_phi[i]) | q
Dgate(d_r[i]) | q
Kgate(kappa[i]) | q
return q
# Create program and engine
prog = sf.Program(1)
eng = sf.Engine('tf', backend_options={"cutoff_dim": cutoff})
# Apply circuit of layers with corresponding depth
with prog.context as q:
for k in range(depth):
layer(k, q[0])
# Run engine
state = eng.run(prog, run_options={"eval": False}).state
ket = state.ket()
#!/usr/bin/env python3
import strawberryfields as sf
from strawberryfields.ops import *
# initialize engine and program objects
eng = sf.Engine(backend="fock", backend_options={"cutoff_dim": 7})
boson_sampling = sf.Program(4)
with boson_sampling.context as q:
# prepare the input fock states
Fock(1) | q[0]
Fock(1) | q[1]
Vac | q[2]
Fock(1) | q[3]
# rotation gates
Rgate(0.5719) | q[0]
Rgate(-1.9782) | q[1]
Rgate(2.0603) | q[2]
Rgate(0.0644) | q[3]
# beamsplitter array
BSgate(0.7804, 0.8578) | (q[0], q[1])
BSgate(0.06406, 0.5165) | (q[2], q[3])
BSgate(0.473, 0.1176) | (q[1], q[2])
BSgate(0.563, 0.1517) | (q[0], q[1])
BSgate(0.1323, 0.9946) | (q[2], q[3])
BSgate(0.311, 0.3231) | (q[1], q[2])
BSgate(0.4348, 0.0798) | (q[0], q[1])
BSgate(0.4368, 0.6157) | (q[2], q[3])
# end circuit
to the output of a GKP qubit phase gate applied to a finite energy |+> state.
The qubit phase gate is applied using measurement-based squeezing."""
import strawberryfields as sf
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import colors, colorbar
sf.hbar = 1
x = np.linspace(-4, 4, 400) * np.sqrt(np.pi)
p = np.linspace(-4, 4, 400) * np.sqrt(np.pi)
wigners = []
# Finite energy GKP |+i> state
prog_y = sf.Program(1)
with prog_y.context as q:
sf.ops.GKP([np.pi / 2, -np.pi / 2], epsilon=0.1) | q[0]
eng = sf.Engine("bosonic")
result = eng.run(prog_y)
wigners.append(result.state.wigner(0, x, p))
# Squeezing values for measurement-based squeezing ancillae
rdB = np.array([14, 10, 6])
r_anc = np.log(10) * rdB / 20
# Detection efficiencies
eta_anc = np.array([1, 0.98, 0.95])
# Target squeezing and rotation angles to apply a GKP phase gate
r = np.arccosh(np.sqrt(5 / 4))
def test_no_unitary(self, tol):
"""Test compilation works with no unitary provided"""
prog = sf.Program(8)
with prog.context as q:
ops.S2gate(SQ_AMPLITUDE) | (q[0], q[4])
ops.S2gate(SQ_AMPLITUDE) | (q[1], q[5])
ops.S2gate(SQ_AMPLITUDE) | (q[2], q[6])
ops.S2gate(SQ_AMPLITUDE) | (q[3], q[7])
ops.MeasureFock() | q
res = prog.compile("X8_01")
expected = sf.Program(8)
with expected.context as q:
ops.S2gate(SQ_AMPLITUDE, 0) | (q[0], q[4])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[1], q[5])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[2], q[6])
ops.S2gate(SQ_AMPLITUDE, 0) | (q[3], q[7])
# corresponds to an identity on modes [0, 1, 2, 3]
# This can be easily seen from below by noting that:
# MZ(pi, pi) = R(0) = I
# MZ(pi, 0) @ MZ(pi, 0) = I
# [R(pi) \otimes I] @ MZ(pi, 0) = I
ops.MZgate(np.pi, 0) | (q[0], q[1])
ops.MZgate(np.pi, 0) | (q[2], q[3])
ops.MZgate(np.pi, np.pi) | (q[1], q[2])
ops.MZgate(np.pi, 0) | (q[0], q[1])
ops.MZgate(np.pi, 0) | (q[2], q[3])
ops.MZgate(np.pi, np.pi) | (q[1], q[2])
ops.Rgate(0) | (q[0])
ops.Rgate(0) | (q[1])
ops.Rgate(0) | (q[2])
ops.Rgate(0) | (q[3])
# corresponds to an identity on modes [4, 5, 6, 7]
ops.MZgate(np.pi, 0) | (q[4], q[5])
ops.MZgate(np.pi, 0) | (q[6], q[7])
ops.MZgate(np.pi, np.pi) | (q[5], q[6])
ops.MZgate(np.pi, 0) | (q[4], q[5])
ops.MZgate(np.pi, 0) | (q[6], q[7])
ops.MZgate(np.pi, np.pi) | (q[5], q[6])
ops.Rgate(0) | (q[4])
ops.Rgate(0) | (q[5])
ops.Rgate(0) | (q[6])
ops.Rgate(0) | (q[7])
ops.MeasureFock() | q
assert program_equivalence(res, expected, atol=tol)
# double check that the applied symplectic is correct
# remove the Fock measurements
res.circuit = res.circuit[:-1]
# extract the Gaussian symplectic matrix
O = res.compile("gaussian_unitary").circuit[0].op.p[0]
# construct the expected symplectic matrix corresponding
# to just the initial two mode squeeze gates
S = TMS(SQ_AMPLITUDE, 0)
expected = np.zeros([2 * 8, 2 * 8])
idx = np.arange(2 * 8).reshape(4, 4).T
for i in idx:
expected[i.reshape(-1, 1), i.reshape(1, -1)] = S
assert np.allclose(O, expected, atol=tol)
