一种与后端无关的MWIS核,用于结合中性原子硬件验证的随机机组组合问题
A Backend-Agnostic MWIS Kernel for Stochastic Unit Commitment with Neutral-Atom Hardware Validation
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中文总结 AI 辅助
本文提出与后端无关的MWIS核,结合中性原子硬件验证解决随机机组组合问题,经50节点15天测试其调度效果达标,144节点时编码稳定,为更大规模组合优化提供路径。
中文摘要 AI 辅助
量子硬件正开始解决结构化组合优化问题,但实际应用仍存在两个阻碍:一是将实际运行模型映射为硬件兼容实例,二是将带噪声的硬件输出转换为可行决策。本文提出一种与后端无关的计算接口,它将随机机组组合的离散决策层编译为基于移动的最大权独立集(MWIS)问题,同时在经典计算层保留连续调度与可行性恢复。我们在绿色氢能调度场景中验证该方法,并将其部署在QuEra Aquila中性原子量子处理器上,这是首个将实际运行决策与可编程中性原子硬件通过求解器无关的MWIS表示连接的端到端工业调度工作流。在针对50节点实例的15天硬件测试中,经经典优化后的硬件生成解在每日均达到或超过精确MWIS所得的调度裕度;当扩展至144节点时,编码质量保持稳定,而完整原子阵列存活概率(而非图嵌入)成为进一步扩展的主要瓶颈。这些结果共同构建了一条通向更大问题规模的硬件兼容计算路径,并为探索精确经典优化可能不再高效的 regime 奠定了基础。
英文摘要
Quantum hardware is beginning to address structured combinatorial optimisation, but two steps still block practical use: mapping real operational models onto hardware-compatible instances, and converting noisy hardware output back into feasible decisions. Here we introduce a backend-agnostic computational interface that compiles the discrete decision layer of stochastic unit commitment into a move-based maximum-weight independent set (MWIS) problem, while retaining continuous dispatch and feasibility recovery in the classical computational layer. We validate the approach in a green hydrogen scheduling setting and deploy it on the QuEra Aquila neutral-atom quantum processor. This is the first end-to-end industrial scheduling workflow that connects real operational decisions to programmable neutral-atom hardware through a solver-agnostic MWIS representation. Across a 15-day hardware campaign on 50-node instances, hardware-generated solutions after classical refinement match or exceed the dispatch margins obtained from exact MWIS on every day. When scaling to 144 nodes, encoding quality remains stable, while the probability that the full atom array survives, rather than graph embedding, emerges as the dominant bottleneck to further scaling. Together, these results establish a hardware-compatible computational pathway toward larger problem scales, and lay the groundwork for exploring regimes in which exact classical optimisation may no longer scale efficiently.
发表机构
- University of Cambridge(剑桥大学)
- China Mobile (Suzhou) Software Technology Co., Ltd.(中国移动(苏州)软件技术有限公司)
- Taiyi Quantum Science & Technology Co. Ltd.(太一量子科技有限公司)
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