Ultrafast neural sampling with spiking nanolasers

Owing to their significant advantages in terms of bandwidth, power efficiency and especially speed, optical neuromorphic systems have arisen as interesting alternatives to conventional semiconductor devices. Recently, photonic crystal nanolasers with excitable behaviour 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.