  Maximum 2-satisfiability (MAX-2-SAT) is a type of combinatorial decision
problem that is known to be NP-hard. In this paper, we compare LightSolver's
quantum-inspired algorithm to a leading deep-learning solver for the MAX-2-SAT
problem. Experiments on benchmark data sets show that LightSolver achieves
significantly smaller time-to-optimal-solution compared to a state-of-the-art
deep-learning algorithm, where the gain in performance tends to increase with
the problem size.
