发表机构
North Carolina State University（北卡罗来纳州立大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对分布式量子计算节点间通信的瓶颈问题，提出基于动态规划的DPRQ量子比特路由算法，经实验验证其相较QuComm可显著减少节点间通信量。
AI 中文摘要
分布式量子计算（DQC）通过克服单个量子处理器的资源限制，为量子计算的规模化提供了一种有前景的方法。然而，由于纠缠分配效率低下且易出错，节点间通信仍是DQC的主要瓶颈。优化节点间通信不仅可以减少执行量子电路所需的纠缠资源量，还能提高结果的执行速度和准确性。本文提出了DPRQ，这是一种用于最小化划分为集体通信块的分布式量子电路中节点间通信的量子比特路由算法。与当前采用贪心块级量子比特路由策略的方法不同，DPRQ采用基于动态规划的技术，专注于全局电路级优化，同时捕获块间依赖关系。我们在四组量子电路和多种DQC配置上对DPRQ进行了评估。结果表明，与最先进的基于集体通信的DQC编译器QuComm相比，DPRQ的创新路由策略在节点间通信上平均实现了24.40%的减少，最大减少幅度达85.06%。
英文摘要
Distributed quantum computing (DQC) offers a promising approach to scale quantum computing by overcoming the resource limitations of a single quantum processor. However, inter-node communication remains a major bottleneck of DQC due to inefficient and error-prone entanglement distribution. Optimizing inter-node communication can not only reduce the amount of entanglement resource needed to execute a quantum circuit but also improve execution speed and accuracy of the results. This paper proposes DPRQ, a qubit routing algorithm for minimizing inter-node communication in distributed quantum circuits divided into collective communication blocks. Unlike current approaches that utilize greedy block-level qubit routing strategies, DPRQ employs a dynamic programming-based technique focused on global circuit-level optimization, while capturing inter-block dependencies. We evaluated DPRQ on four sets of quantum circuits and a variety of DQC configurations. The results demonstrate that DPRQ's innovative routing strategy achieves an average of 24.40% reduction with a maximum of 85.06% reduction in inter-node communication, when compared to the state-of-the-art collective communication-based DQC compiler QuComm.
Comments6 pages, 4 figures
Journal refProc. 3rd ACM SIGCOMM Workshop on Quantum Networks and Distributed Quantum Computing (QuNet' 26), 2026, pp. 68-73
