arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2503.07331quant-phcs.ET
收藏

TANGO:一种基于两阶段搜索与双向前瞻的鲁棒量子比特映射算法

TANGO: A Robust Qubit Mapping Algorithm via Two-Stage Search and Bidirectional Look

Kang Xu, Yukun Wang, Dandan Li

AI总结:

TANGO算法通过层级权重分配、两阶段路由搜索和双向前瞻SWAP策略,在多种量子设备上协同优化门数量与电路深度,提升了量子电路映射的保真度。

摘要:

当前的量子设备通常缺乏完整的量子比特连通性,使得在量子设备上直接执行逻辑电路变得困难。这一局限性要求量子电路映射算法插入SWAP门,动态地将逻辑量子比特重新映射到物理量子比特,并将逻辑电路转换为符合设备连通性约束的物理电路。然而,SWAP门的插入会增加门数量和电路深度,最终降低量子算法的保真度。为了实现这两个目标的均衡优化,我们提出了TANGO算法。通过引入层级权重分配策略,该算法首先构建了一个评估函数,用以平衡量子比特映射对已映射节点和未映射节点的影响,从而提升初始映射的质量。接着,我们设计了一种创新的两阶段路由算法,该算法以可执行门数量作为主要评估指标,同时考虑量子门距离、电路深度以及一种新颖的双向前瞻SWAP策略,该策略结合前置门来优化SWAP门的选择,从而提高映射算法的有效性。最后,通过集成先进的量子门优化技术,算法的整体性能得到进一步增强。实验结果表明,与最先进的方法相比,所提算法在各种基准和量子设备上实现了门数量和电路深度的多目标协同优化,展现出显著的性能优势。

英文摘要:

Current quantum devices typically lack full qubit connectivity, making it difficult to directly execute logical circuits on quantum devices. This limitation necessitates quantum circuit mapping algorithms to insert SWAP gates, dynamically remapping logical qubits to physical qubits and transforming logical circuits into physical circuits that comply with device connectivity constraints. However, the insertion of SWAP gates increases both the gate count and circuit depth, ultimately reducing the fidelity of quantum algorithms. To achieve a balanced optimization of these two objectives, we propose the TANGO algorithm. By incorporating a layer-weight allocation strategy, the algorithm first formulates an evaluation function that balances the impact of qubit mapping on both mapped and unmapped nodes, thereby enhancing the quality of the initial mapping. Next, we design an innovative two-stage routing algorithm that prioritizes the number of executable gates as the primary evaluation metric while also considering quantum gate distance, circuit depth, and a novel bidirectional-look SWAP strategy, which optimizes SWAP gate selection in conjunction with preceding gates, improving the effectiveness of the mapping algorithm. Finally, by integrating advanced quantum gate optimization techniques, the algorithm's overall performance is further enhanced. Experimental results demonstrate that, compared to state-of-the-art methods, the proposed algorithm achieves multi-objective co-optimization of gate count and circuit depth across various benchmarks and quantum devices, exhibiting significant performance advantages.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑