Lightsolver challenges a leading deep learning solver for Max-2-SAT problems

TL;DR

This paper compares LightSolver's quantum-inspired algorithm to a deep-learning solver for MAX-2-SAT, demonstrating that LightSolver achieves faster solutions, especially as problem size increases.

Contribution

The paper introduces LightSolver, a quantum-inspired algorithm that outperforms existing deep-learning methods on MAX-2-SAT problems in terms of solution time.

Findings

LightSolver achieves significantly smaller time-to-solution.

Performance gap increases with problem size.

LightSolver outperforms deep-learning solver on benchmark datasets.

Abstract

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.