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IEEE Conference on Computer Communications (INFOCOM)

Joint Optimization of Circuit Transformation and Qubit Mapping for Distributed Quantum Computing

Xiangzhi Zhang, Xu Xu, Ying Liu, Yingling Mao, 肖斌, Yuanyuan Yang

Published May 18, 2026• 2 citations
Publisher
DOI
10.1109/infocom59046.2026.11571664

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Distributed quantum computing (DQC) architectures offer a scalable solution for the computational demands of large-scale quantum computing. In near-term DQC architectures, the costly remote quantum communication and the execution cost within quantum chips together significantly limit the execution efficiency of quantum circuits. To comprehensively optimize both costs, we propose DMapS, which consists of end-to-end algorithms for qubit mapping and routing. The qubit mapping component, DMapS-M, adopts a two-stage mapping strategy that decomposes a large quantum circuit into smaller ones and parallelizes the qubit mapping on quantum chips. The qubit routing component, DMapS-R, reduces remote quantum communication overhead by prioritizing the insertion of local SWAP gates and further improves transpilation efficiency by exploiting parallelism within chips. Our experimental results show that DMapS-M reduces overall overhead (including both remote quantum communication overhead and local SWAP gate overhead) by an average of 43.44% and 59.72%, respectively, compared to two baseline algorithms, and achieves an average speedup of 87.05x. DMapS-R, compared to the baseline algorithm, reduces overall overhead by an average of 8.85% and achieves an average transpilation speedup of 2.78×. Moreover, compared to the DQC-oriented quantum compiler, DMapS reduces remote communication overhead by an average of 75.16%.
Efficiently mapping quantum programs onto Distributed quantum computing (DQC) are challenging, particularly when considering the heterogeneous quantum processing units (QPUs) with different structures. In this paper, we present a comprehensive compilation framework that addresses these challenges with three key insights: exploiting structural patterns within quantum circuits, using clustering for initial qubit placement, and adjusting qubit mapping with annealing algorithms. Experimental results demonstrate the effectiveness of our methods and the capability to handle complex heterogeneous distributed quantum systems. Our evaluation shows that our method reduces the objective value at most 88.40% compared to the baseline.
Distributed quantum computing (DQC) mitigates the hardware limitations of near-term devices by partitioning large circuits into smaller segments executed on qubit-limited processors. However, even these reduced circuits often exceed the capacity of near-term devices that provide only extremely few reliable qubits. To address this, we introduce a qubit-reuse based DQC framework that exploits mid-circuit measurement and reset to enable scalable execution with minimal physical qubits. We consider distributed quantum learning (DQL) as a representative case study and formulate an energy minimization problem for the proposed framework that jointly optimizes quantum operation energy, measurement repetitions, and transmission power. To tackle the inherent non-convexity, we design a hybrid block coordinate descent (BCD) and successive convex approximation (SCA) algorithm for efficient solution. Extensive simulations on MNIST and CIFAR-10 demonstrate that the proposed qubit-reuse framework enables scalable execution on qubit-limited devices while achieving competitive performance compared to centralized quantum computing, and that the joint optimization algorithm significantly improves energy efficiency over baseline schemes.