无需训练的量子自动编码器，实现无监督异常检测
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
- 利用量子自动编码器构建无监督异常检测框架
- 不依赖任何训练过程，直接通过量子态差异识别异常
- 适用于金融、医疗等对异常敏感的高可靠性场景
检测关键任务中的异常事件和数据是金融、医疗、能源等多个行业的重要挑战。量子计算近年来成为解决多种机器学习任务的强大工具，但训练量子机器学习模型仍面临困难，尤其是梯度计算难题。这一挑战在异常检测中尤为突出，因为无监督学习方法对实际应用至关重要。为此，我们提出 Quorum，首个专为无监督学习设计的量子异常检测框架，无需任何训练即可运行。
原文摘要 · Abstract (English)
Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum computing has recently emerged as a powerful tool for tackling several machine learning tasks, but training quantum machine learning models remains challenging, particularly due to the difficulty of gradient calculation. The challenge is even greater for anomaly detection, where unsupervised learning methods are essential to ensure practical applicability. To address these issues, we propose Quorum, the first quantum anomaly detection framework designed for unsupervised learning that operates without requiring any training.
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