  Massively parallel annealing processors may offer superior performance for a
wide range of sampling and optimization problems. A key component dictating the
size of these processors is the neuron update circuit, ideally implemented
using special stochastic nanodevices. We leverage photon statistics using
single photon avalanche diodes (SPADs) and temporal filtering to generate
stochastic states. This method is a powerful alternative offering unique
features not currently seen in annealing processors: the ability to
continuously control the computational temperature and the seamless extension
to the Potts model, a $n$-state generalization of the two-state Ising model.
SPADs also offer a considerable practical advantage since they are readily
manufacturable in current standard CMOS processes. As a first step towards
realizing a CMOS SPAD-based annealer, we have designed Ising and Potts models
driven by an array of discrete SPADs and show they accurately sample from their
theoretical distributions.
