Optimizing Compilation for Distributed Quantum Computing via Clustering and Annealing

• URL: http://arxiv.org/abs/2508.15267v1
• Date: Thu, 21 Aug 2025 06:00:24 GMT
• Title: Optimizing Compilation for Distributed Quantum Computing via Clustering and Annealing
• Authors: Ruilin Zhou, Jinglei Cheng, Yuhang Gan, Junyu Liu, Chen Qian,
• Abstract summary: We present a comprehensive compilation framework that addresses these challenges.<n>We exploit structural patterns within quantum circuits, using clustering for initial qubit placement, and adjusting qubit mapping.<n>Our method reduces the objective value at most 88.40% compared to the baseline.
• Score: 14.652241553662327
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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