Quorum：使用量子自动编码器的零训练无监督异常检测
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
AI总结：
针对量子机器学习模型训练困难及异常检测需无监督方法的问题，提出首个无需训练的无监督量子异常检测框架Quorum，利用量子计算解决多行业关键异常检测挑战。
摘要：
检测关键任务异常事件和数据是金融、医疗保健和能源等多个行业的关键挑战。量子计算最近成为解决多项机器学习任务的强大工具，但训练量子机器学习模型仍具挑战性，尤其是梯度计算困难。异常检测的挑战更大，无监督学习方法对确保实际适用性至关重要。为解决这些问题，我们提出Quorum，首个专为无监督学习设计且无需任何训练的量子异常检测框架。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
