AI 中文总结
该研究针对量子退火常受局部极小值限制的问题，引入数学框架理解能量与汉明距离联系，构建ZZ催化剂，通过小无挫折子问题基态模式使远离解的构型竞争力减弱，在稀疏问题等上有效果且可调节。
AI 中文摘要
量子退火常常受限于陷入局部极小值的种群，其与解有许多自旋翻转。我们引入一个数学框架来理解优化问题中能量与汉明距离之间的联系。利用此框架，我们从小的无挫折子问题的基态模式构建ZZ催化剂，使远离解的构型在能量上竞争力减弱。在稀疏问题上，它们在短扫描时成倍增加接近解的概率，在全连接模型上也有增益且可通过子问题选择进行调节。
英文摘要
Quantum annealing is often limited by population trapped in local minima many spin flips from the solution. We introduce a mathematical framework to understand the connection between energy and Hamming distance in optimization problems. Using this, we build ZZ-catalysts from ground-state patterns of small frustration-free subproblems that make configurations far from the solution less energetically competitive. On sparse problems they multiply the near-solution probability at short sweeps, with gains persisting on fully-connected models and tunable via subproblem choice.
Comments20 pages, 13 figures and 2 tables
