探索逆向退火中知识迁移的潜力，提升优化问题求解效率。
Transfer of Knowledge through Reverse Annealing: A Preliminary Analysis of the Benefits and What to Share
- 用逆向退火机制在相似问题间迁移优质解
- 14/16项背包问题实验显示成功率提升显著
- 适合研究量子退火与优化算法融合的学者
当前处于NISQ时代，量子退火器在高效求解优化问题方面仍存在局限。为缓解此问题，D-Wave系统提出逆向退火机制，一种针对已有良好状态进行局部优化的量子退火方法。尽管相关研究活跃，但尚未有理论探讨其在知识迁移方面的潜在优势。本文通过实验探究两个核心问题：一、逆向退火能否从相似问题间实现知识迁移？二、能否识别出有助于提升成功概率的初始解特征？为此，选取经典的背包问题作为基准，共使用34个实例，包含14项和16项的组合，进行系统测试与分析。
原文摘要 · Abstract (English)
Being immersed in the NISQ-era, current quantum annealers present limitations for solving optimization problems efficiently. To mitigate these limitations, D-Wave Systems developed a mechanism called Reverse Annealing, a specific type of quantum annealing designed to perform local refinement of good states found elsewhere. Despite the research activity around Reverse Annealing, none has theorized about the possible benefits related to the transfer of knowledge under this paradigm. This work moves in that direction and is driven by experimentation focused on answering two key research questions: i) is reverse annealing a paradigm that can benefit from knowledge transfer between similar problems? and ii) can we infer the characteristics that an input solution should meet to help increase the probability of success? To properly guide the tests in this paper, the well-known Knapsack Problem has been chosen for benchmarking purposes, using a total of 34 instances composed of 14 and 16 items.
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