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.

翻译：摘要：尽管量子神经网络在决策系统中取得了成功，但其公平性仍未被探索，因为焦点主要在于准确性。本工作通过设计空间探索揭示了量子神经网络的不公平性，并强调了量子神经网络部署和量子噪声对准确性与公平性的显著影响。为有效驾驭庞大的量子神经网络部署设计空间，我们提出了JustQ——一个在NISQ计算机上部署公平且准确量子神经网络的框架。该框架包含完整的NISQ误差模型、基于强化学习的部署方案，以及兼顾公平性与准确性的灵活优化目标。实验结果表明，JustQ在准确性和公平性上均优于先前方法，取得了更优性能。本工作开创了NISQ计算机上公平量子神经网络设计的先河，为未来研究铺平道路。