Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware

This work presents a noise-aware distributed QAOA framework designed to overcome the limitations of Noisy Intermediate-Scale Quantum (NISQ) hardware. Key innovations include:

• Problem decomposition across multiple QPUs to bypass limited qubit counts.
• Error mitigation techniques that enhance qubit fidelity and gate operation accuracy in noisy conditions.
• Benchmarking using the HamilToniQ toolkit, validating improved speed and accuracy across different quantum hardware platforms.

The proposed strategy advances practical quantum optimization by combining hardware-aware design with distributed execution, paving the way toward scalable and efficient quantum computing in the NISQ era.

Recommended citation: Chen, Kuan-Cheng; Xu, Xiaotian; Burt, Felix; Liu, Chen-Yu; Yu, Shang; & Leung, Kin K. (2024). "Noise-Aware Distributed Quantum Approximate Optimization Algorithm on Near-term Quantum Hardware." Proceedings of the 2024 IEEE International Conference on Quantum Computing and Engineering (QCE).
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