Riemannian optimization for linear optical problems
Abstract
Many applications of linear optics in quantum science involve searching for some optimal configuration of a multiport interferometer. For example, to find heralded state preparations, one can optimize the interferometer to maximize the fidelity and success probability of the state preparation. In this paper, we derive a closed analytical formula for the Riemannian gradient of any differentiable function defined over the interferometer unitaries. We use this gradient in Riemannian optimization algorithms, such as gradient descent or BFGS, to find the optimal setup of the interferometer. As an interesting use case, we search for linear optical state preparations, improving some of the success probabilities reported in the literature for NOON or photon catalysis preparations. Finally, we show that our optimizer is orders of magnitude faster than other methods in the literature, opening the door for solving linear optical problems involving more modes and photons.
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BibTeX
@misc{parellada2026riemannian,
title = {{Riemannian optimization for linear optical problems}},
author = {Pablo V. Parellada},
howpublished = {arXiv},
year = {2026},
month = sep,
doi = {10.48550/arxiv.2609.07890},
eprint = {2609.07890},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2609.07890},
note = {CC BY-SA 4.0},
}
RIS
TY - UNPB
TI - Riemannian optimization for linear optical problems
AU - Pablo V. Parellada
PY - 2026
DA - 2026/09/17
PB - arXiv
DO - 10.48550/arxiv.2609.07890
UR - https://arxiv.org/abs/2609.07890
AB - Many applications of linear optics in quantum science involve searching for some optimal configuration of a multiport interferometer. For example, to find heralded state preparations, one can optimize the interferometer to maximize the fidelity and success probability of the state preparation. In this paper, we derive a closed analytical formula for the Riemannian gradient of any differentiable function defined over the interferometer unitaries. We use this gradient in Riemannian optimization algorithms, such as gradient descent or BFGS, to find the optimal setup of the interferometer. As an interesting use case, we search for linear optical state preparations, improving some of the success probabilities reported in the literature for NOON or photon catalysis preparations. Finally, we show that our optimizer is orders of magnitude faster than other methods in the literature, opening the door for solving linear optical problems involving more modes and photons.
ER -