arXiv:2302.06926quant-ph
Lightsolver challenges a leading deep learning solver for Max-2-SAT problems
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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.
> In three recent trials testing the performance and accuracy of the new LPU, the device showed promise against its supercomputing and quantum computing counterparts in the following ways: They proceed to cite three not-yet-peer-reviewed manuscripts by the co-founders [1,2,3].…
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