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 出版日期:2026-03-24 发布日期:2026-04-01
  • 通讯作者: Yu-Xin Deng E-mail:yxdeng@msg.sufe.edu.cn

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 Online:2026-03-24 Published:2026-04-01
  • Contact: Yu-Xin Deng E-mail:yxdeng@msg.sufe.edu.cn

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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)