  In this article, we describe an algorithm for solving Quadratic Unconstrained
Binary Optimization problems on the Intel Loihi 2 neuromorphic processor. The
solver is based on a hardware-aware fine-grained parallel simulated annealing
algorithm developed for Intel's neuromorphic research chip Loihi 2. Preliminary
results show that our approach can generate feasible solutions in as little as
1 ms and up to 37x more energy efficient compared to two baseline solvers
running on a CPU. These advantages could be especially relevant for size-,
weight-, and power-constrained edge computing applications.
