发表机构
University of Cambridge（剑桥大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对伊辛机耦合场离散化导致的动态失真，提出通用度量及缓解协议（全局偏置与有色噪声），在复杂能量景观中将成功率提升两至三个数量级。
AI 中文摘要
尽管伊辛机（IMs）因其作为组合优化问题（COPs）的物理加速器的潜力而日益流行，但大多数文献侧重于经验性基准测试，其普适性较低。在本工作中，我们提出动态反馈失真作为通用度量，用于量化时间复用伊辛机中耦合场离散化的影响。从实际硬件的传递函数出发，我们解析地展示了如何通过全局偏置（或选择正确的离散耦合元件）以及注入有色噪声来缓解这种失真。我们的数值结果表明，我们的缓解协议非常有效，尤其是在复杂的能量景观（即可调莫比乌斯阶梯和二维平铺问题）中；其中，失真的模拟减少使得复杂COPs的成功率提高了两到三个数量级。
英文摘要
Although Ising machines (IMs) have grown in popularity due to their potential as physics-based accelerators for combinatorial optimization problems (COPs), most literature focuses on empirically-obtained benchmarks with low universality. In this work, we propose dynamic feedback distortion as a universal metric to quantify the effect of coupling field discretization in time-multiplexed IMs. Starting from the transfer functions of realistic hardware, we analytically show how this distortion can be mitigated using a global bias (or choosing the correct discrete coupling element) and injecting coloured noise. Our numerical results show that our mitigation protocols are very effective, especially in complicated energy landscapes (namely tunable Möbius ladders and 2D tiling problems); where an analogue reduction of the distortion leads to a two--three order of magnitude increase in the success rate of complex COPs.
