Gauging practical computational advantage using a classical, threshold-based Gaussian boson sampler
Abstract
We describe an efficient, scalable Gaussian boson sampler based on a classical description of squeezed quantum light and a deterministic model of single-photon detectors that click when the incident amplitude falls above a given threshold. Using this model, we map several NP-Complete graph theoretic problems to equivalent Gaussian boson sampling problems and numerically explore the practical efficacy of our approach. Specifically, for a given weighted, undirected graph we examined finding the densest k-subgraph and the maximum weighted clique. We also examined the graph classification problem. Compared to traditional classical solvers, we found that our method provides better solutions in a comparable amount of samples for graphs with up to 2000 nodes.
Cite
@article{arxiv.2507.17567,
title = {Gauging practical computational advantage using a classical, threshold-based Gaussian boson sampler},
author = {Sarvesh Raghuraman and Aditya Patwardhan and Brian La Cour},
journal= {arXiv preprint arXiv:2507.17567},
year = {2025}
}
Comments
9 pages, 12 figures. To be published in QCE 2025