Aug 2026· Proceedings of the 3rd ACM SIGCOMM Workshop on Quantum Networks and Distributed Quantum Computing· 0 citations· 21 references
PhysicsComputer Science
TL;DR
DPRQ is proposed, a qubit routing algorithm for minimizing inter-node communication in distributed quantum circuits divided into collective communication blocks that employs a dynamic programming-based technique focused on global circuit-level optimization, while capturing inter-block dependencies.
Abstract
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.
Distributed Quantum Computing (DQC) enables the execution of quantum circuits across multiple interconnected quantum processing units (QPUs) but requiring efficient qubit allocation and network topology design to optimize computational performance. Proper qubit allocation minimizes entanglement costs across QPUs, balan...
Ji-Yao Liu, Lei Fan, Yuan-Xiong Guo et al.· IEEE Transactions on Network...· 0 citations
The realization of practical quantum advantage requires executing large-scale circuits that far exceed the qubit capacity of any single quantum processor. To address this, two primary scaling strategies have emerged: circuit cutting, which utilizes classical resources to decompose circuits into smaller fragments, and m...
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It is shown that the best-performing compilation strategy varies across the tested circuits and network configurations, and that both network topology and intra-QPU connectivity substantially affect the entanglement cost of execution.
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