Lightsolver challenges a leading deep learning solver for Max-2-SAT problems
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Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 7 of its 30 references that are in the map. First posted on arXiv 14 Feb 2023.
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7 of 30 references.
References · 7
- 2018Learning a SAT Solver from Single-Bit Supervisioncited by 200
- 2018Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Searchcited by 183
- 2018Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSPcited by 58
- 2019Graph Neural Networks for Maximum Constraint Satisfactioncited by 46
- 2019Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problemscited by 40
- 2021Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimizationcited by 5
- 2022Quantum Sparse Codingcited by 3
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