应用量子自编码器进行时间序列异常检测
Applying Quantum Autoencoders for Time Series Anomaly Detection
AI总结：
该研究将量子自编码器应用于时间序列异常检测，通过重构误差分析和潜在表示分析两种技术，在模拟和真实量子硬件上均验证了其相比经典深度学习自编码器参数量更少、训练迭代次数更少且性能更优的优势。
摘要：
异常检测是一个重要问题，在欺诈检测、模式识别或医学诊断等多个领域都有应用。目前已有多种采用经典计算方法的算法被提出。然而，利用量子计算解决时间序列数据中的异常检测问题仍是一个尚未被广泛探索的研究领域。本文探索了量子自编码器在时间序列异常检测中的应用。我们研究了两种主要的异常分类技术：（1）分析量子自编码器产生的重构误差，（2）潜在表示分析。我们在多种ansatz（量子线路结构）上开展的模拟实验结果表明，在多个数据集上，量子自编码器的性能始终优于基于经典深度学习的自编码器。具体而言，量子自编码器在实现更优异常检测性能的同时，参数量减少了60-230倍，训练迭代次数减少了5倍。此外，我们在真实量子硬件上实现了我们的量子编码器。实验结果表明，量子自编码器在真实硬件上的异常检测性能与模拟版本相当。
英文摘要：
Anomaly detection is an important problem with applications in various domains such as fraud detection, pattern recognition or medical diagnosis. Several algorithms have been introduced using classical computing approaches. However, using quantum computing for solving anomaly detection problems in time series data is a widely unexplored research field. This paper explores the application of quantum autoencoders to time series anomaly detection. We investigate two primary techniques for classifying anomalies: (1) Analyzing the reconstruction error generated by the quantum autoencoder and (2) latent representation analysis. Our simulated experimental results, conducted across various ansaetze, demonstrate that quantum autoencoders consistently outperform classical deep learning-based autoencoders across multiple datasets. Specifically, quantum autoencoders achieve superior anomaly detection performance while utilizing 60-230 times fewer parameters and requiring five times fewer training iterations. In addition, we implement our quantum encoder on real quantum hardware. Our experimental results demonstrate that quantum autoencoders achieve anomaly detection performance on par with their simulated counterparts.
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。
