Universal photonic neural networks with quantum-free data reuploading

Keisuke Kojima; Toshiaki Koike-Akino

doi:10.1117/12.3023231

2 August 2024 Universal photonic neural networks with quantum-free data reuploading

Keisuke Kojima, Toshiaki Koike-Akino

Proceedings Volume 13106, Photonics for Quantum 2024; 1310607 (2024) https://doi.org/10.1117/12.3023231
Event: Photonics for Quantum, 2024, Waterloo, Ontario, Canada

Conference Poster

Abstract

The data reuploading trick was originally proposed for quantum computing to achieve the universal approximation property. In this paper, we introduce data reuploading to realize universal non-quantum photonic computing with practical photonic integrated circuits (PICs). We aim to comprehensively discuss the various advantages and implementation considerations of this approach. Our framework can eliminate the need of quantum squeezed lights, photon counters, and nonlinear photonics, which have been essential for enabling photonic neural networks in conventional configurations. Additionally, we explore ways to minimize the optical components by combining multiple functionalities into a single phase shifter, showing competitive performance when compared to using the same number of phase shifters, all without employing any nonlinear photonic devices. Considering these characteristics, our investigation into the use of PICs for data reuploading presents a novel architectural approach to realize photonic neural networks. This approach embodies unique features that distinctly set it apart from traditional photonic neural networks.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Keisuke Kojima and Toshiaki Koike-Akino "Universal photonic neural networks with quantum-free data reuploading", Proc. SPIE 13106, Photonics for Quantum 2024, 1310607 (2 August 2024); https://doi.org/10.1117/12.3023231

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KEYWORDS

Neural networks

Photonic integrated circuits

Quantum data

Phase shifts

Quantum light

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