QuST: Optimizing Quantum Neural Network Against Spatial and Temporal Noise Biases

Quantum neural networks (QNNs) hold immense potential for complex tasks by harnessing quantum entanglement and superposition, such as physics simulation, artificial intelligence, and cryptography. However, the presence of quantum noise, stemming from hardware imperfections and environmental interactions, significantly reduces their practical performance. Moreover, the noise varies from different devices and shifts over time, necessitating continuous retraining models to chase and cater to the evolving noise, leading to high-computation costs. In this article, we present QuST, a novel QNN robust training framework designed to handle the noise in a once-and-for-all manner, which can tackle both spatial and temporal biases to maintain the QNN model accuracy under ever-changing noise conditions. Our approach consists of three key components. First, we propose a metric called circuit sequence correctness (CSC) to characterize QNN circuit reliability in noisy environments. Then, we model CSC as a training weight to incorporate loss integration and utilize KL divergence to align noise inference with noise-free inference, thereby improving anti-noise capabilities. Furthermore, we introduce multiscale noise-aware training to enhance the model’s noise tolerance at various noise magnitudes. We conduct experiments on MNIST and fashion-MNIST datasets, along with 190-day historical noise simulations and one case study on 7 real IBMQ quantum computers. The results demonstrate 8.1%–15.1% and 9.1%–11.45% accuracy improvements in temporal and spatial dimensions, respectively. Additionally, we conduct ablation experiments to validate the effectiveness of the QuST’s key components. The results demonstrate that QuST consistently sustains high accuracy without retraining,even under changing noise conditions, and exhibits minimal loss of accuracy as noise levels increase.