中国物理B ›› 2026, Vol. 35 ›› Issue (4): 40309-040309.doi: 10.1088/1674-1056/ae00ae

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Hierarchical QAOA circuit design framework for distributed quantum computing

Ting-Yu Luo(骆挺宇)1 and Yu-Xin Deng(邓玉欣)1,2,†   

  1. 1 Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China;
    2 School of Computing and Artificial Intelligence, Shanghai University of Finance and Economics, Shanghai 200433, China
  • 收稿日期:2025-06-25 修回日期:2025-08-13 接受日期:2025-08-29 发布日期:2026-04-01
  • 通讯作者: Yu-Xin Deng E-mail:yxdeng@msg.sufe.edu.cn
  • 基金资助:
    Project supported by the National Natural Science Foundation of China (Grant No. 62472175), Shanghai Trusted Industry Internet Software Collaborative Innovation Center, and the “Digital Silk Road” Shanghai International Joint Laboratory of Trustworthy Intelligent Software (Grant No. 22510750100).

Hierarchical QAOA circuit design framework for distributed quantum computing

Ting-Yu Luo(骆挺宇)1 and Yu-Xin Deng(邓玉欣)1,2,†   

  1. 1 Shanghai Key Laboratory of Trustworthy Computing, East China Normal University, Shanghai 200062, China;
    2 School of Computing and Artificial Intelligence, Shanghai University of Finance and Economics, Shanghai 200433, China
  • Received:2025-06-25 Revised:2025-08-13 Accepted:2025-08-29 Published:2026-04-01
  • Contact: Yu-Xin Deng E-mail:yxdeng@msg.sufe.edu.cn
  • Supported by:
    Project supported by the National Natural Science Foundation of China (Grant No. 62472175), Shanghai Trusted Industry Internet Software Collaborative Innovation Center, and the “Digital Silk Road” Shanghai International Joint Laboratory of Trustworthy Intelligent Software (Grant No. 22510750100).

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摘要： The quantum approximate optimization algorithm (QAOA) is a promising approach for solving combinatorial optimization problems on real quantum devices. As QAOA scales to tackle larger problem instances, the limited qubit capacity of single-chip systems becomes a critical bottleneck. To overcome this limitation, distributed quantum computing (DQC) provides a scalable solution. However, when QAOA circuits are executed in such systems, their performance is significantly hindered by the high cost of remote communication. Motivated by this challenge, we propose $HiQ$-$DF$, a QAOA circuit design framework tailored for DQC systems. By employing a hierarchical optimization strategy, $HiQ$-$DF$ enables comprehensive multi-objective optimization during circuit construction. Experimental results on QAOA circuits solving MaxCut instances show that our framework significantly outperforms baseline methods, achieving an average reduction of $26.12%$ in EPR pair usage (up to $36.85%$), $26.44%$ in circuit latency (up to $35.27%$), and $39.63%$ in circuit depth (up to $49.3%$).

关键词: quantum circuit design, quantum approximate optimization algorithm, distributed quantum computing

Abstract: The quantum approximate optimization algorithm (QAOA) is a promising approach for solving combinatorial optimization problems on real quantum devices. As QAOA scales to tackle larger problem instances, the limited qubit capacity of single-chip systems becomes a critical bottleneck. To overcome this limitation, distributed quantum computing (DQC) provides a scalable solution. However, when QAOA circuits are executed in such systems, their performance is significantly hindered by the high cost of remote communication. Motivated by this challenge, we propose $HiQ$-$DF$, a QAOA circuit design framework tailored for DQC systems. By employing a hierarchical optimization strategy, $HiQ$-$DF$ enables comprehensive multi-objective optimization during circuit construction. Experimental results on QAOA circuits solving MaxCut instances show that our framework significantly outperforms baseline methods, achieving an average reduction of $26.12%$ in EPR pair usage (up to $36.85%$), $26.44%$ in circuit latency (up to $35.27%$), and $39.63%$ in circuit depth (up to $49.3%$).

Key words: quantum circuit design, quantum approximate optimization algorithm, distributed quantum computing

中图分类号:  (Quantum computation architectures and implementations)

  • 03.67.Lx
03.67.Ac (Quantum algorithms, protocols, and simulations) 85.25.-j (Superconducting devices)