  Biological neural networks effortlessly tackle complex computational problems
and excel at predicting outcomes from noisy, incomplete data, a task that poses
significant challenges to traditional processors. 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 quantification. Since both the chaotic light source and the
photonic crossbar support multiple independent computational wavelength
channels, we sample from the output distributions in parallel at a sampling
rate of 70.4 GS/s, limited only by the electronic interface. We exploit the
photonic probabilistic architecture to simultaneously perform image
classification and uncertainty prediction via a Bayesian neural network. Our
prototype demonstrates the seamless cointegration of a physical entropy source
and a computational architecture that enables ultrafast probabilistic
computation by parallel sampling.
