arXiv Preprint Reports Polynomial-Time Classical Simulation of Gaussian Boson Sampling on Graphs, Challenging Quantum Advantage Claims

June 28, 2026

arXiv Unknown

Overview

An arXiv preprint reports a classical algorithm capable of simulating Gaussian Boson Sampling (GBS) on graphs in polynomial time. While GBS was previously proposed as a potential demonstration of quantum advantage with boson computers, this study argues for the existence of an efficient classical algorithm under specific conditions. This finding has significant implications for the rigor of quantum advantage proofs, typically based on classically hard distributions, urging a re-evaluation of the boundaries of quantum computational superiority.

In Depth

Key Findings

A new preprint article published on arXiv demonstrates that Gaussian Boson Sampling (GBS) problems on graph structures can be efficiently simulated using a classical algorithm that operates in polynomial time. This finding challenges previous claims regarding the quantum advantage of GBS under specific conditions and holds significant implications for redefining the boundaries of ‘quantum advantage’ or ‘quantum supremacy’ in quantum computing.

Technical / Clinical Details

Gaussian Boson Sampling (GBS) is a task performed by quantum devices known as boson computers, which use photons to generate samples from a specific probability distribution. Traditionally, GBS, at certain scales and complexities, was considered difficult for classical computers to simulate efficiently, making it a strong candidate for demonstrating quantum advantage. The classical algorithm presented in this research mathematically proves that by leveraging specific graph structures—such as graph density or connectivity properties—the GBS process can be efficiently reproduced in polynomial time. This implies that for certain types of input graphs, the output of a boson sampling machine can be approximated by classical computation. Specifically, it suggests that the extent to which classical computing power can mimic the performance of quantum devices might be broader than previously thought. This study emphasizes the necessity for more rigorous consideration of problem settings and classical algorithm performance when evaluating the robustness of quantum advantage claims.

Background & Context

In the field of quantum computing, demonstrating ‘Quantum Supremacy/Advantage’ has been a major milestone, signifying that a quantum computer can overwhelmingly outperform classical computers on specific computational tasks. Prominent examples include Google’s Sycamore processor for random circuit sampling and China’s Jiuzhang for GBS demonstrations. However, these demonstrations are always subject to re-evaluation of their claimed superiority as classical algorithms advance. The discovery of classical algorithms like the one presented in this research suggests that a deeper exploration into the limits of classical computing power is necessary to make quantum advantage claims more robust.

Strategic Significance & Outlook

The suggestion that GBS on graphs can be classically simulated in polynomial time raises important discussions within the quantum computing R&D community. It underscores that demonstrations of quantum advantage need to be based on problems that are more universal and inherently cannot be solved efficiently by classical approaches. Moving forward, researchers will likely pursue even more advanced classical algorithms to challenge other quantum computational advantage claims, including GBS. This process will help define more clearly the intrinsic value offered by truly practical quantum computers and the nature of the problems they are poised to solve, guiding quantum technology investment and development towards more effective directions.

Source: https://arxiv.org/html/2511.16558v2

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