{"ID":2835227,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2512.09936","arxiv_id":"2512.09936","title":"QSTAformer: A Quantum-Enhanced Transformer for Robust Short-Term Voltage Stability Assessment against Adversarial Attacks","abstract":"Short-term voltage stability assessment (STVSA) is critical for secure power system operation. While classical machine learning-based methods have demonstrated strong performance, they still face challenges in robustness under adversarial conditions. This paper proposes QSTAformer-a tailored quantum-enhanced Transformer architecture that embeds parameterized quantum circuits (PQCs) into attention mechanisms-for robust and efficient STVSA. A dedicated adversarial training strategy is developed to defend against both white-box and gray-box attacks. Furthermore, diverse PQC architectures are benchmarked to explore trade-offs between expressiveness, convergence, and efficiency. To the best of our knowledge, this is the first work to systematically investigate the adversarial vulnerability of quantum machine learning-based STVSA. Case studies on the IEEE 39-bus system demonstrate that QSTAformer achieves competitive accuracy, reduced complexity, and stronger robustness, underscoring its potential for secure and scalable STVSA under adversarial conditions.","short_abstract":"Short-term voltage stability assessment (STVSA) is critical for secure power system operation. While classical machine learning-based methods have demonstrated strong performance, they still face challenges in robustness under adversarial conditions. This paper proposes QSTAformer-a tailored quantum-enhanced Transforme...","url_abs":"https://arxiv.org/abs/2512.09936","url_pdf":"https://arxiv.org/pdf/2512.09936v1","authors":"[\"Yang Li\",\"Chong Ma\",\"Yuanzheng Li\",\"Sen Li\",\"Yanbo Chen\",\"Zhaoyang Dong\"]","published":"2025-11-29T16:45:46Z","proceeding":"eess.SY","tasks":"[\"eess.SY\",\"cs.LG\",\"quant-ph\"]","methods":"[\"Transformer\"]","has_code":false}