HeteroQNN: Enabling Distributed QNN Under Heterogeneous Quantum Devices

Liqiang Lu, Tianyao Chu, Siwei Tan, Jingwen Leng, Fangxin Liu, Congliang Lang, Yifan Guo, Jianwei Yin

Abstract: In the current NISQ era, the performance of quantum neural network (QNN) models is strictly hindered by the limited qubit number and inevitable noise. A natural idea to improve the robustness of QNN is the implementation of a distributed system. Nevertheless, due to the heterogeneity and instability of quantum chips (e.g., noise, frequent online/offline), training and inference on distributed quantum devices may even destroy the accuracy. In this article, we propose HeteroQNN, a comprehensive QNN framework designed for efficient and high-accuracy distributed training and inference. The main innovation of HeteroQNN is it decouples the QNN circuit into two uniform representations: model vector and behavioral vector. The model vector specifies the gate parameters in the QNN model, while the behavioral vector captures the hardware features when implementing the QNN circuit. To handle the architectural heterogeneity, we introduce personalized QNN models in each quantum processing unit (QPU) and share the gradient among QPUs with homogeneous behavioral vectors. We propose shot-oriented distributed inference, which is much more fine-grained scheduling that can improve accuracy and balance the workload. Finally, by leveraging the hidden homogeneity in the model vector, we present the maintenance for QPU variability. The experiments show that HeteroQNN accelerates the training process by $4.03 \times $ with 7.87% loss reduction, compared with the previous distributed QNN framework.

External IDs:doi:10.1109/tcad.2025.3588457