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

arXiv: https://arxiv.org/abs/2302.06926 · HTML: https://mlsanity.com/paper/2302.06926/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 7 of 7)

- [Learning a SAT Solver from Single-Bit Supervision](https://mlsanity.com/paper/1802.03685/) (2018)
- [Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search](https://mlsanity.com/paper/1810.10659/) (2018)
- [Learning to Solve NP-Complete Problems - A Graph Neural Network for Decision TSP](https://mlsanity.com/paper/1809.02721/) (2018)
- [Graph Neural Networks for Maximum Constraint Satisfaction](https://mlsanity.com/paper/1909.08387/) (2019)
- [Graph Colouring Meets Deep Learning: Effective Graph Neural Network Models for Combinatorial Problems](https://mlsanity.com/paper/1903.04598/) (2019)
- [Benchmark of quantum-inspired heuristic solvers for quadratic unconstrained binary optimization](https://mlsanity.com/paper/2104.14096/) (2021)
- [Quantum Sparse Coding](https://mlsanity.com/paper/2209.03788/) (2022)

## Built on by (most cited first, 1 of 1)

- [Binarizing Physics-Inspired GNNs for Combinatorial Optimization](https://mlsanity.com/paper/2507.13703/) (2025)

## 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.