AI 中文总结
该研究提出结合图感知精确归约与工程化工具包的方法，优化静态量子比特分配的分支定界搜索过程，在单线程和多线程场景下均实现显著加速，且能快速证明Boeblingen–Cairo实验所有实例为最优。
AI 中文摘要
静态量子比特分配将电路的逻辑量子比特映射到稀疏物理器件，同时最小化交互加权的物理距离代价函数，该问题可转化为矩形二次分配问题。现有研究结合了强下界与分布式分支定界法，我们将图感知精确归约与工程化工具包整合，形成轻量级分配-边界路径，包含不可避免分配代价过滤、增量维护的根轨道与前缀稳定器对称性剪枝、条件父LAP筛选以及与电路无关的物理器件特性。在21个相对简单的Melbourne实例和GLB基线完成的6个Boeblingen实例上，最终单线程配置分别实现了2.98倍和13.27倍的几何平均加速；在一台共享内存服务器上使用60个线程时，最终Boeblingen–Cairo实验中的所有实例均在半小时内被证明为最优，不包含一次性器件工件构建。这些结果表明，图感知节点处理与搜索过程的工程化大幅降低了精确分配所需的资源。
英文摘要
Static qubit allocation maps a circuit's logical qubits to a sparse physical device while minimising an interaction-weighted physical-distance cost function, yielding a rectangular quadratic assignment problem. Existing work combines strong lower bounds with distributed branch-and-bound. We integrate graph-aware exact reductions with an engineering bundle for a lightweight assignment-bound path: unavoidable assigned-cost filtering, incrementally maintained root-orbit and prefix-stabilizer symmetry pruning, conditioned parent-LAP screening, and circuit-independent physical device profiles. On 21 relatively easy Melbourne instances and six Boeblingen instances completed by the GLB baseline, the final single-thread configuration provides geometric-mean speedups of 2.98x and 13.27x, respectively. With 60 threads on one shared-memory server, all instances in the final Boeblingen--Cairo experiment are certified optimal within half an hour, excluding one-time device-artifact construction. These results show that graph-aware node processing and engineering the search process substantially reduce the resources required for exact allocation.
