Gauging practical computational advantage using a classical, threshold-based Gaussian boson sampler

Sarvesh Raghuraman; Aditya Patwardhan; Brian La Cour

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arXiv · 2507.17567

Gauging practical computational advantage using a classical, threshold-based Gaussian boson sampler

Sarvesh Raghuraman·Aditya Patwardhan·Brian La Cour

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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.

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Sarvesh Raghuraman, Aditya Patwardhan, Brian La Cour. 2025-07-23. Gauging practical computational advantage using a classical, threshold-based Gaussian boson sampler. https://arxiv.org/abs/2507.17567

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