Probabilistic photonic computing with chaotic light.

ABSTRACT: Biological neural networks effortlessly tackle complex computational problems and excel at predicting outcomes from noisy, incomplete data. Artificial neural networks (ANNs), inspired by these biological counterparts, have emerged as powerful tools for deciphering intricate data patterns and making predictions. However, conventional ANNs can be viewed as "point estimates" that do not capture the uncertainty of prediction, which is an inherently probabilistic process. In contrast, treating an ANN as a probabilistic model derived via Bayesian inference poses significant challenges for conventional deterministic computing architectures. Here, we use chaotic light in combination with incoherent photonic data processing to enable high-speed probabilistic computation and uncertainty quantificati

SUBMITTER: Bruckerhoff-Pluckelmann F 

PROVIDER: S-EPMC11609296 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature