ArbiterQ: Improving QNN Convergency and Accuracy by Applying Personalized Model on Heterogeneous Quantum Devices

In the current NISQ era, the performance of QNN models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is to involve multiple quantum devices. Nevertheless, due to the heterogeneity and instability of quantum devices (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this paper, we propose ArbiterQ, a comprehensive QNN framework designed for efficient and high-accuracy training and inference on heterogeneous QPUs. The main innovation of ArbiterQ is it applies personalized models for each QPU via two uniform QNN representations: model vector and behavioral vector. The model vector specifies the logical-level parameters in the QNN model, while the behavioral vector captures the hardware-level features when implementing the QNN circuit. In this manner, by sharing the gradient among QPUs with similar behavioral vectors, we can effectively leverage parallelism while considering heterogeneity. We also propose shot-oriented inference scheduling, which is a much more fine-grained scheduling that can improve accuracy and balance the workload. The experiments show that ArbiterQ accelerates the training process by $4.03 \times$ with $7.87 \%$ loss reduction, compared with the previous distributed QNN framework EQC [1].