Classical digital computer architectures are visibly approaching their technological and physical limits. Thus, there is a growing interest in developing post-digital computing approaches to overcome these limitations. Besides quantum computers, approaches that emulate neuromorphic processes represent a promising alternative because they mimic the massively parallel, energy-efficient computations carried out by the human brain. Such computations constitute the building blocks of the pattern recognition algorithms underpinning the success of machine learning (ML). Optically integrated systems promise 2–3 orders of magnitude higher energy efficiency compared to today's electronic approaches. Among others, post-digital computer concepts will enable numerous new applications for ML in places like data centers or security systems, as well as autonomous vehicles, drones and satellites – any area where massive amounts of computations need to be done but is limited by power and time. In this project we will realize ML with optical neural networks. That is, we want to use light to power machine learning, due to the potential advantages that an optical neural network (ONN) has over one that is emulated on conventional GPU chips. Moreover, we will investigate the potential of neuromorphic computing hardware for ML on low-power autonomous systems.
- E. Robertson, L. Jaruingue, L. Messner, L. Esguerra, G. Gallego, K. Lüdge, J. Wolters. A scheme for optical reservoir computers with atomic memory. (Poster presentation), Hot Vapor Workshop, Stuttgart/Online, 22-24 March 2021.
- L. Jaurigue, E. Robertson, J. Wolters, and K. Lüdge (2021). Reservoir computing with delayed input for fast and easy optimisation. Entropy, 23, 1560. https://doi.org/10.3390/e23121560 [Preprint]
- L. Esguerra, L. Meßner, E. Robertson, N. V. Ewald, M. Gündoğan, and J. Wolters (2022). Optimization and readout-noise analysis of a hot vapor EIT memory on the Cs D1 line. Quantum Physics. https://doi.org/10.48550/arXiv.2203.06151 [Preprint]