Efficient training of a 128-modes 3D continuously coupled photonic chip via limited quantum resources
Abstract
Integrated photonics provides a promising route towards the development of large-scale, stable and low-loss quantum hardware. Existing implementations of universal quantum photonic chips generally rely on architectures consisting of localised interactions between the modes that can be programmed with known algorithms to implement specific operations. Recent advances have shown that interferometers based on 3D continuously coupled waveguide arrays may allow to significantly scale up the number of modes with reduced overhead. However, for this class of devices, optimal strategies to set the controllable parameters and obtain the desired transformation have not been demonstrated generally. In this work, we report the use of a machine learning algorithm driven by quantum data to program the operation of a reconfigurable three-dimensional continuously coupled photonic device comprising 128 modes. We show experimentally the capability of reaching a target unitary matrix, among those achievable by the device, through a limited number of one- and two-photon measurements. Our results demonstrate that this approach results to be effective in the presence of noise and with a limited amount of resources, also allowing to partially compensate imperfections in the experimental setup. This method can thus represent a viable route towards programming large scale integrated photonic devices, unlocking the possibility of controlling photonic systems for which an analytical decomposition is not trivial.
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BibTeX
@misc{stanev2026efficient,
title = {{Efficient training of a 128-modes 3D continuously coupled photonic chip via limited quantum resources}},
author = {Denis Stanev and Simone Di Micco and Francesco Hoch and Taira Giordani and Nicolò Spagnolo and Marco Gardina and Andrea Crespi and Francesco Ceccarelli and Roberto Osellame and Fabio Sciarrino},
howpublished = {arXiv},
year = {2026},
month = nov,
doi = {10.48550/arxiv.2611.05681},
eprint = {2611.05681},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2611.05681},
}
RIS
TY - UNPB
TI - Efficient training of a 128-modes 3D continuously coupled photonic chip via limited quantum resources
AU - Denis Stanev
AU - Simone Di Micco
AU - Francesco Hoch
AU - Taira Giordani
AU - Nicolò Spagnolo
AU - Marco Gardina
AU - Andrea Crespi
AU - Francesco Ceccarelli
AU - Roberto Osellame
AU - Fabio Sciarrino
PY - 2026
DA - 2026/11/03
PB - arXiv
DO - 10.48550/arxiv.2611.05681
UR - https://arxiv.org/abs/2611.05681
AB - Integrated photonics provides a promising route towards the development of large-scale, stable and low-loss quantum hardware. Existing implementations of universal quantum photonic chips generally rely on architectures consisting of localised interactions between the modes that can be programmed with known algorithms to implement specific operations. Recent advances have shown that interferometers based on 3D continuously coupled waveguide arrays may allow to significantly scale up the number of modes with reduced overhead. However, for this class of devices, optimal strategies to set the controllable parameters and obtain the desired transformation have not been demonstrated generally. In this work, we report the use of a machine learning algorithm driven by quantum data to program the operation of a reconfigurable three-dimensional continuously coupled photonic device comprising 128 modes. We show experimentally the capability of reaching a target unitary matrix, among those achievable by the device, through a limited number of one- and two-photon measurements. Our results demonstrate that this approach results to be effective in the presence of noise and with a limited amount of resources, also allowing to partially compensate imperfections in the experimental setup. This method can thus represent a viable route towards programming large scale integrated photonic devices, unlocking the possibility of controlling photonic systems for which an analytical decomposition is not trivial.
ER -