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Efficient Ground-State Manifold Amplification for Rydberg Atom Arrays via Houdayer Cluster Updates

Andrew J. Ochoa, Martin J. A. Schuetz, Helmut G. Katzgraber · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28528 · cited by 0 works (OpenAlex) · open-access PDF

Chinese

虽然我们正在演示一个混合量子经典方法来探索最多独立组的问题的地面多样性,其中量子硬件提供初始解决方案,并且一个完全经典的后处理算法扩展它们,增加了量子硬件测量的解决方案多样性,高达几种尺寸。量子优化设备在六种尺寸上,虽然能够生成高质量的后处理解决方案,但可能遭受根本的样本限制。这对于那些高度依赖于各种不同不相关的最佳解决方案的应用程序尤其具有问题。我们将Houdayer集合更新算法应用于系统地扩展量子生成的解决方案,通过经典的后处理解决方案。我们在我们的方法上验证了来自Quera Aquila的简单模拟设备的43个问题实例的测试床,从而证明了多样性

Machine translation (Meta M2M100); the English original follows.

Abstract

We demonstrate a hybrid quantum-classical approach for exploring ground-state manifolds of maximum independent set problems, in which quantum hardware supplies the initial solutions and an entirely classical post-processing algorithm expands them, increasing the solution diversity of quantum hardware measurements by up to several orders of magnitude. Quantum optimization devices, while capable of generating high-quality solutions, can suffer from fundamental sampling limitations. This is particularly problematic for applications that strongly depend on a large variety of uncorrelated optimal solutions. We apply the Houdayer cluster update algorithm to systematically expand quantum-generated solution sets through classical post-processing. We validate our approach on a test bed of 43 problem instances from QuEra's Aquila analog annealing device, achieving diversity amplification factors, defined as the increase in the number of distinct solutions at a given set size, of up to 11923x with a median of 24.3x across different problem scales and hardness parameters. Because exact ground-state degeneracies are known for these instances, the recovered fraction of the manifold can be measured rather than bounded: post-processing recovers a median of 21% of the ground-state manifold, and provably all of it for six instances. We also demonstrate that, in some cases, the classical post-processing can discover independent sets larger than those found by the quantum hardware, thus improving on solution quality and not just diversity. While the method is non-ergodic and cannot guarantee complete manifold coverage, it consistently discovers a larger number of high-quality, and in some cases strictly better, configurations than quantum sampling alone. This work demonstrates how simple classical post-processing can enhance the utility of quantum optimization results.

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Topics: Quantum, Quantum annealing, Algorithm, Manifold (fluid mechanics), Set (abstract data type), Mathematics

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