# Ultrafast neural sampling with spiking nanolasers

## TL;DR

The paper proposes using spiking nanolasers in optical systems to perform fast and efficient Bayesian inference, potentially outperforming digital systems in speed and power.

## Contribution

The novel contribution is demonstrating how spiking photonic crystal nanolasers can be used for Bayesian inference through sampling.

## Key findings

- Photonic spiking networks can perform Bayesian inference by sampling from learned probability distributions.
- Translation rules from conventional sampling networks to photonic spiking networks are derived and validated.
- Estimated processing speed and power consumption show potential improvements of several orders of magnitude over current systems.

## Abstract

Owing to their significant advantages in terms of bandwidth, power efficiency, and latency, optical neuromorphic systems have arisen as interesting alternatives to digital electronic devices. Recently, photonic crystal nanolasers with excitable behavior were first demonstrated. Depending on the pumping strength, they emit short optical pulses – spikes – at various intervals on a nanosecond timescale. In this theoretical work, we show how networks of such photonic spiking neurons can be used for Bayesian inference through sampling from learned probability distributions. We provide a detailed derivation of translation rules from conventional sampling networks, such as Boltzmann machines, to photonic spiking networks and demonstrate their functionality across a range of generative tasks. Finally, we provide estimates of processing speed and power consumption, for which we expect improvements of several orders of magnitude over current state-of-the-art neuromorphic systems.

Optical neuromorphic systems promise significant advantages in terms of bandwidth, power efficiency, and speed. Here, authors demonstrate how networks of spiking photonic crystal nanolasers can be trained to perform Bayesian inference through sampling from multivariate probability distributions.