Analog photonic neural networks for large-scale AI at the quantum limit

Logan G. Wright; Peter L. McMahon; Tatsuhiro Onodera; Tianyu Wang

doi:10.1117/12.2680563

28 September 2023 Analog photonic neural networks for large-scale AI at the quantum limit

Logan G. Wright, Peter L. McMahon, Tatsuhiro Onodera, Tianyu Wang

Author Affiliations +

Proceedings Volume PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023; PC1265510 (2023) https://doi.org/10.1117/12.2680563
Event: SPIE Nanoscience + Engineering, 2023, San Diego, California, United States

Abstract

I will overview our work on analog neural networks based on photonics and other controllable physical systems. I will show how backpropagation can efficiently train physical neural networks (PNNs), and how to design physical network architectures for physics-based machine learning. I will review our work showing how nonlinear photonic neural networks may enhance computational sensing and how photonic neural networks may be operated robustly deep into low-energy regimes where quantum noise would ordinarily be a limiting factor. Finally, I will show that PNNs offer fundamental advantages for scaling AI models such as Transformers.

Conference Presentation

Citation Download Citation

Logan G. Wright, Peter L. McMahon, Tatsuhiro Onodera, and Tianyu Wang "Analog photonic neural networks for large-scale AI at the quantum limit", Proc. SPIE PC12655, Emerging Topics in Artificial Intelligence (ETAI) 2023, PC1265510 (28 September 2023); https://doi.org/10.1117/12.2680563

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