Develop effective learning algorithms for Physical Machine Learning (PML) and physical learning

Develop effective learning algorithms for physical machine learning that either (i) efficiently configure the controllable hardware parameters θ using a separate processor or (ii) realize on-chip physical learning in which the physical hardware itself performs the optimization of its own parameters.

Background

Physical machine learning (PML) seeks to co-design algorithms and hardware by directly optimizing physical hardware parameters so that the hardware’s intrinsic dynamics compute desired input–output mappings. In this paradigm, input vectors are encoded into a hardware system, which transforms them according to its physical evolution, and outputs are read from measured degrees of freedom; trainable parameters influence this transformation.

However, current PML demonstrations typically achieve only simple tasks due to two core difficulties: challenging optimization landscapes (e.g., barren plateaus) and a pronounced simulation-to-reality (sim2real) gap that undermines purely simulation-based training. Consequently, there is a need for learning algorithms that can either configure hardware parameters efficiently from a separate processor or, ideally, leverage the hardware itself to carry out parameter updates (physical learning).