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IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems

A Quantum Circuit Optimization Framework for Distributed Quantum Computing

Fengsheng Liu, Fudong Liu, Yangyang Fei, Hong Wang, Junchao Wang, Haodong Jiang, Zhi Ma

Published Jan 21, 2026• 0 citations
Publisher
DOI
10.1109/tcad.2026.3656760

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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%).
Distributed quantum computing (DQC) that allows a large quantum circuit to be executed simultaneously on multiple quantum processing units (QPUs) becomes a promising approach to increase the scalability of quantum computing. It is natural to envision the near-future DQC platform as a multi-tenant cluster of QPUs, called a Quantum Cloud. However, no existing DQC work has addressed the two key problems of running DQC in a multi-tenant quantum cloud: placing multiple quantum circuits to QPUs and scheduling network resources to complete these jobs. This work is the first attempt to design a circuit placement and resource scheduling framework for a multi-tenant environment. The proposed framework is called CloudQC, which includes two main functional components, circuit placement and network scheduler, with the objectives of optimizing both quantum network cost and quantum computing time. Experimental results with real quantum circuit workloads show that CloudQC significantly reduces the average job completion time compared to existing DQC placement algorithms for both single-circuit and multi-circuit DQC. We envision this work will motivate more future work on network-aware quantum cloud.
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