JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks
<|reference_start|>JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks: Despite the success of Quantum Neural Networks (QNNs) in decision-making systems, their fairness remains unexplored, as the focus primarily lies on accuracy. This work conducts a design space exploration, unveiling QNN unfairness, and highlighting the significant influence of QNN deployment and quantum noise on accuracy and fairness. To effectively navigate the vast QNN deployment design space, we propose JustQ, a framework for deploying fair and accurate QNNs on NISQ computers. It includes a complete NISQ error model, reinforcement learning-based deployment, and a flexible optimization objective incorporating both fairness and accuracy. Experimental results show JustQ outperforms previous methods, achieving superior accuracy and fairness. This work pioneers fair QNN design on NISQ computers, paving the way for future investigations.<|reference_end|>
@article{wang2024justq:,
    title={JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks},
    author={Ruhan Wang, Fahiz Baba-Yara, Fan Chen},
    journal={published at ASP-DAC 2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.11048},
    primaryClass={quant-ph cs.CY cs.LG}
}