AI 中文总结
本研究提出首个脏量子位借用调度器Bona，其通过感知深度启发式算法实现，可平均减少近99%的脏辅助量子位，在并行量子行走等场景中兼具窄宽度与浅深度的优化优势。
AI 中文摘要
辅助量子位的管理已成为减小量子电路宽度的关键技术。脏辅助量子位可从任何临时空闲的量子位借用，无需考虑其初始状态，为宽度优化提供了极大灵活性，但目前其使用仍需人工处理，易出错。我们将脏量子位借用问题形式化，通过证明其NP-hard性确立了该问题的基本计算限制。为支持实际优化，我们提出了Bona，这是首个用于脏量子位借用的调度器，基于一种新颖的感知深度启发式算法构建。我们在多种基准上评估了Bona，包括实际量子电路和真实电路模块的随机排列组合，发现它平均减少了近99%的脏辅助量子位，且深度开销可控。特别地，对于并行量子行走——并行哈密顿模拟的关键组件，Bona达到了Jiang等人2024年提出的清洁量子位方案以及Quantinuum公司方案的电路宽度，但获得了显著更小的电路深度，为脏辅助量子位在具有特定并行性的电路中提供了独特的优化优势提供了具体证据。
英文摘要
The management of ancilla qubits has become a critical technique for reducing quantum circuit width. Dirty ancillas, which may be borrowed from any temporarily idle qubit regardless of their initial states, offer substantial flexibility for width optimization, but their use has so far required manual and error-prone handling. We formalize the dirty-qubit borrowing problem and establish a fundamental computational limit by proving its NP-hardness. To support practical optimization, we present \bona, the first scheduler for dirty-qubit borrowing, built on a novel depth-aware heuristic algorithm. We evaluate \bona~ across a variety of benchmarks, including practical quantum circuits and randomly arranged compositions of real circuit modules, and find that it reduces nearly 99\% of dirty ancillas on average with controlled depth overhead. In particular, for parallel quantum walk---an essential component of parallel Hamiltonian simulation---\bona~ matches the circuit width achieved by the clean-qubit schemes of \citeauthor{jiang2024recycling}~(\citeyear{jiang2024recycling}) and \citeauthor{quantinuum}~(\citeyear{quantinuum}), but attains significantly smaller circuit depth, providing concrete evidence that dirty ancillas offer unique optimization advantages in circuits with certain parallelism.
