JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks
Ruhan Wang Fahiz Baba-Yara Fan Chen
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.