Quantum Federated Learning Based Power System Stability Assessment: A Noise Robust Approach

The integration of distributed energy resources requires effective distributed transient stability assessments (TSA) that can capture complex stability dynamics through localized data processing. In this paper, we propose a Quantum Vertical Federated Learning (QVFL)-enabled TSA framework. Our core innovation is a quantum average pooling-based distributed TSA method (QdTSA-AP), which enables data-driven transient stability prediction in a resource-efficient manner while being robustness-by-design against quantum noise, thus ensuring practical applicability in the current Noisy Intermediate-Scale Quantum (NISQ) era. Key contributions include: 1) A quantum average pooling method for effective information extraction from local TSA data while minimizing the communication overhead of transferring quantum information; 2) A lightweight noise-robustness evaluation method, which, for the first time, applies the Signal-to-Noise Ratio (SNR) concept from signal processing to assess the noise robustness of QdTSA-AP, which can be generalized to a wide range of quantum machine learning (QML) tasks; 3) A theoretical proof, based on SNR, showing that average pooling enhances the noise resilience of QVFL through its architectural design rather than requiring additional computational overhead. Extensive numerical experiments validate the accuracy and noise robustness of QdTSA-AP and the effectiveness of the proposed average pooling strategy.