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arXiv · 2006.13541

Neural Schr\"{o}dinger Equation:Physical Law as Neural Network

Mitsumasa Nakajima·Kenji Tanaka·Toshikazu Hashimoto
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Abstract

We show a new family of neural networks based on the Schr\"{o}dinger equation (SE-NET). In this analogy, the trainable weights of the neural networks correspond to the physical quantities of the Schr\"{o}dinger equation. These physical quantities can be trained using the complex-valued adjoint method. Since the propagation of the SE-NET can be described by the evolution of physical systems, its outputs can be computed by using a physical solver. As a demonstration, we implemented the SE-NET using the finite difference method. The trained network is transferable to actual optical systems. Based on this concept, we show a numerical demonstration of end-to-end machine learning with an optical frontend. Our results extend the application field of machine learning to hybrid physical-digital optimizations.

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BibTeXRIS

Mitsumasa Nakajima, Kenji Tanaka, Toshikazu Hashimoto. 2020-06-24. Neural Schr\"{o}dinger Equation:Physical Law as Neural Network. https://doi.org/10.1109/tnnls.2021.3120472

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