Training physical systems like neural networks

I will overview our recent work on training controllable physical systems to perform machine learning calculations using the backpropagation algorithm. This has the potential to enable machines that are "learned" similar to neural networks, which may be useful for computational sensing or energy-efficient machine learning. Works referenced: Wright, L. G., Onodera, T., Stein, M. M., Wang, T., Schachter, D. T., Hu, Z., & McMahon, P. L. (2022). Deep physical neural networks trained with backpropagation. Nature, 601(7894), 549-555. Senanian, A., Prabhu, S., Kremenetski, V., Roy, S., Cao, Y., Kline, J., Onodera, T., Wright, L.G., Wu, X., Fatemi, V. and McMahon, P.L., 2023. Microwave signal processing using an analog quantum reservoir computer. arXiv preprint arXiv:2312.16166. Wang, T., Sohoni, M. M., Wright, L. G., Stein, M. M., Ma, S. Y., Onodera, T., ... & McMahon, P. L. (2023). Image sensing with multilayer nonlinear optical neural networks. Nature Photonics, 17(5), 408-415.