Spatial photonic machines for large-scale computing and polarization imaging
Davide Pierangeli · 2024
Photonic machines based on spatial optics are promising for optimization and machine learning at a large scale and enable novel functionalities across photonics. We present the topic with a review of our work on three main paradigms. The first is the spatial photonic Ising machine (SPIM), which exploits spatial light modulation and coherent optical propagation to solve hard combinatorial optimization problems by taking advantage of optical parallelism and scalability. The second is the photonic extreme learning machine (PELM) based on free-space optics. We show a large-scale experimental implementation with half a million addressable nodes, which allows us to perform photonic machine learning in the so-called over-parametrized region and to implement photonic natural language processing. Finally, we exploit the computing principle of spatial photonic learning machines to demonstrate single-shot polarization imaging. This original method enables a new fast and compact polarization camera for the many contexts where conventional polarimetry is unavailable, thus opening new possibilities in imaging and optical communication.