  Finding the maximum cut of a graph (MAXCUT) is a classic optimization problem
that has motivated parallel algorithm development. While approximate algorithms
to MAXCUT offer attractive theoretical guarantees and demonstrate compelling
empirical performance, such approximation approaches can shift the dominant
computational cost to the stochastic sampling operations. Neuromorphic
computing, which uses the organizing principles of the nervous system to
inspire new parallel computing architectures, offers a possible solution. One
ubiquitous feature of natural brains is stochasticity: the individual elements
of biological neural networks possess an intrinsic randomness that serves as a
resource enabling their unique computational capacities. By designing circuits
and algorithms that make use of randomness similarly to natural brains, we
hypothesize that the intrinsic randomness in microelectronics devices could be
turned into a valuable component of a neuromorphic architecture enabling more
efficient computations. Here, we present neuromorphic circuits that transform
the stochastic behavior of a pool of random devices into useful correlations
that drive stochastic solutions to MAXCUT. We show that these circuits perform
favorably in comparison to software solvers and argue that this neuromorphic
hardware implementation provides a path for scaling advantages. This work
demonstrates the utility of combining neuromorphic principles with intrinsic
randomness as a computational resource for new computational architectures.
