> In three recent trials testing the performance and accuracy of the new LPU, the device showed promise against its supercomputing and quantum computing counter

> 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]. In all three papers, they use a simulation of their LPU (that runs on CPU or GPU), and compare to some classical heuristics on an AWS instance. I read [1] and the results are... preliminary at best. They compare their simulation to a single deep-learning-based heuristic called RUN-CSP on tiny MAX-2-SAT instances (<350 variables). Deep learning is great but it is generally not SOTA on combinatorial problems. Still, RUN-CSP is not bad, and its authors seem more reasonable; in their paper, they write: "Despite being generic, we show that [RUN-CSP] matches or surpasses most greedy and semi-definite programming based algorithms and sometimes even outperforms state-of-the-art heuristics for the specific problems." Note that RUN-CSP is indeed generic and was by no mean fine-tuned for MAX-2-SAT. In summary, LightSolver only exists as a simulator on GPUs, that simulator was only tested on very niche problems, and even then probably not against the state-of-the-art. I am trying to be polite here, but to be clear: the BS-o-meter is strongly in the red. [1] https://arxiv.org/pdf/2302.06926.pdf https://arxiv.org/pdf/2302.06926.pdf [2] https://arxiv.org/pdf/2207.09517.pdf https://arxiv.org/pdf/2207.09517.pdf [3] https://arxiv.org/pdf/2209.03788.pdf https://arxiv.org/pdf/2209.03788.pdf