Lightsolver challenges a leading deep learning solver for Max-2-SAT problems
This addresses combinatorial optimization challenges for researchers and practitioners, but it is incremental as it compares to existing methods without introducing a new paradigm.
The paper tackled the NP-hard MAX-2-SAT problem by comparing LightSolver's quantum-inspired algorithm to a leading deep-learning solver, finding that LightSolver achieves significantly smaller time-to-optimal-solution, with performance gains increasing with problem size.
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.