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62 changes: 42 additions & 20 deletions mrmustard/physics/wigner.py
Original file line number Diff line number Diff line change
@@ -1,11 +1,8 @@
# Copyright 2022 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.
# You may obtain a copy of the License at

# http://www.apache.org/licenses/LICENSE-2.0

# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
Expand All @@ -26,16 +23,27 @@
# Helpers
# ~~~~~~~


@njit(cache=True)
def make_grid(q_vec, p_vec, hbar): # pragma: no cover
r"""Returns two coordinate matrices `Q` and `P` from coordinate vectors
`q_vec` and `p_vec`, along with the grid over which Wigner functions can be
discretized.
"""
Q = np.outer(q_vec, np.ones_like(p_vec))
P = np.outer(np.ones_like(q_vec), p_vec)
return Q, P, (Q + P * 1.0j) / np.sqrt(2 * hbar)
n_q = q_vec.size
n_p = p_vec.size
Q = np.empty((n_q, n_p), dtype=np.float64)
P = np.empty((n_q, n_p), dtype=np.float64)
sqrt_factor = 1.0 / np.sqrt(2.0 * hbar)

for i in range(n_q):
q = q_vec[i]
for j in range(n_p):
p = p_vec[j]
Q[i, j] = q
P[i, j] = p

grid = (Q + 1j * P) * sqrt_factor
return Q, P, grid


@njit(cache=True)
Expand Down Expand Up @@ -105,6 +113,15 @@ def wigner_discretized(rho, q_vec, p_vec):
method = settings.DISCRETIZATION_METHOD

rho = math.asnumpy(rho)

q_vec = np.asarray(q_vec)
p_vec = np.asarray(p_vec)

if q_vec.ndim == 0:
q_vec = np.array([q_vec])
if p_vec.ndim == 0:
p_vec = np.array([p_vec])

if method == "iterative":
return _wigner_discretized_iterative(rho, q_vec, p_vec, hbar)
return _wigner_discretized_clenshaw(rho, q_vec, p_vec, hbar)
Expand Down Expand Up @@ -143,31 +160,36 @@ def _wigner_discretized_clenshaw(rho, q_vec, p_vec, hbar): # pragma: no cover

@njit(cache=True)
def _wigner_discretized_iterative(rho, q_vec, p_vec, hbar): # pragma: no cover
"""Optimized iterative computation of the Wigner function."""
cutoff = len(rho)
Q, P, grid = make_grid(q_vec, p_vec, hbar)
Wmat = np.zeros((2, cutoff, *grid.shape), dtype=np.complex128)

# W = rho(0,0)W(|0><0|)
Wmat[0, 0] = np.exp(-2.0 * np.abs(grid) ** 2) / np.pi
W = np.real(rho[0, 0]) * np.real(Wmat[0, 0])
# Precompute the exponential term to avoid recalculating it.
exp_grid = np.exp(-2.0 * np.abs(grid) ** 2) / np.pi

for n in range(1, cutoff):
Wmat[0, n] = (2.0 * grid * Wmat[0, n - 1]) / np.sqrt(n)
# Initialize Wmat and W with the |0><0| component.
Wmat[0, 0] = exp_grid
W = rho[0, 0].real * Wmat[0, 0].real

# Precompute square roots to avoid repetitive calculations.
sqrt_n = np.array([np.sqrt(n) for n in range(cutoff)], dtype=np.float64)

# W += rho(0,n)W(|0><n|) + rho(n,0)W(|n><0|)
# Compute the first set of Wigner coefficients.
for n in range(1, cutoff):
Wmat[0, n] = (2.0 * grid * Wmat[0, n - 1]) / sqrt_n[n]
W += 2 * np.real(rho[0, n] * Wmat[0, n])

# Compute the remaining coefficients and accumulate the Wigner function.
for m in range(1, cutoff):
Wmat[1, m] = (2 * np.conj(grid) * Wmat[0, m] - np.sqrt(m) * Wmat[0, m - 1]) / np.sqrt(m)

# W = rho(m, m)W(|m><m|)
W += np.real(rho[m, m] * Wmat[1, m])
Wmat[1, m] = (2 * np.conj(grid) * Wmat[0, m] - sqrt_n[m] * Wmat[0, m - 1]) / sqrt_n[m]
W += rho[m, m].real * Wmat[1, m].real

for n in range(m + 1, cutoff):
Wmat[1, n] = (2 * grid * Wmat[1, n - 1] - np.sqrt(m) * Wmat[0, n - 1]) / np.sqrt(n)

# W += rho(m,n)W(|m><n|) + rho(n,m)W(|n><m|)
Wmat[1, n] = (2 * grid * Wmat[1, n - 1] - sqrt_n[m] * Wmat[0, n - 1]) / sqrt_n[n]
W += 2 * np.real(rho[m, n] * Wmat[1, n])

# Swap the matrices to reuse memory without copying.
Wmat[0] = Wmat[1]

return W / hbar, Q, P