Boltzmann强化学习用于类比Ising机器中的噪声鲁棒性
Boltzmann Reinforcement Learning for Noise resilience in Analog Ising Machines
AI总结：
BRAIN通过变分强化学习实现类比Ising机器中的噪声鲁棒性，提升基态保真度并加速求解过程。
摘要：
类比Ising机器（AIMs）已经作为一种有前景的求解组合优化问题的范式出现，利用物理动力学以高能效解决Ising问题。然而，传统优化和采样算法在这些平台上的性能常常受到固有测量噪声的限制。我们介绍了BRAIN（Boltzmann强化学习用于类比Ising网络），一种利用变分强化学习近似Boltzmann分布的分布学习框架。通过从按状态采样转向在多个嘈杂测量中聚合信息，BRAIN对AIMs中固有的高斯噪声具有鲁棒性。我们评估了BRAIN在多种组合拓扑结构上的性能，包括Curie-Weiss和二维最近邻Ising系统。我们发现，在现实中的3%高斯测量噪声下，BRAIN保持98%的基态保真度，而马尔可夫链蒙特卡洛（MCMC）方法降级到51%保真度。此外，在这些条件下，BRAIN达到MCMC等效解的速度快192倍。BRAIN在65536个自旋上表现出O(N^1.55)的扩展性，并在40%的严重测量不确定性下保持稳健性。除了基态优化外，BRAIN准确捕捉热力学相变和亚稳态，提供了一种可扩展且噪声鲁棒的方法，用于在复杂优化中利用类比计算架构。
英文摘要：
Analog Ising machines (AIMs) have emerged as a promising paradigm for combinatorial optimization, utilizing physical dynamics to solve Ising problems with high energy efficiency. However, the performance of traditional optimization and sampling algorithms on these platforms is often limited by inherent measurement noise. We introduce BRAIN (Boltzmann Reinforcement for Analog Ising Networks), a distribution learning framework that utilizes variational reinforcement learning to approximate the Boltzmann distribution. By shifting from state-by-state sampling to aggregating information across multiple noisy measurements, BRAIN is resilient to Gaussian noise characteristic of AIMs. We evaluate BRAIN across diverse combinatorial topologies, including the Curie-Weiss and 2D nearest-neighbor Ising systems. We find that under realistic 3\% Gaussian measurement noise, BRAIN maintains 98\% ground state fidelity, whereas Markov Chain Monte Carlo (MCMC) methods degrade to 51\% fidelity. Furthermore, BRAIN reaches the MCMC-equivalent solution up to 192x faster under these conditions. BRAIN exhibits $\mathcal{O}(N^{1.55})$ scaling up to 65,536 spins and maintains robustness against severe measurement uncertainty up to 40\%. Beyond ground state optimization, BRAIN accurately captures thermodynamic phase transitions and metastable states, providing a scalable and noise-resilient method for utilizing analog computing architectures in complex optimizations.
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