JustQ: Automated Deployment of Fair and Accurate Quantum Neural Networks
This work pioneers fair QNN design on NISQ computers, addressing fairness issues in quantum decision-making systems for researchers and practitioners in quantum computing.
The authors tackled the problem of fairness in Quantum Neural Networks (QNNs) by proposing JustQ, a framework for deploying fair and accurate QNNs on NISQ computers, which outperforms previous methods in achieving superior accuracy and fairness.
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.