用量子自编码器检测时间序列异常，参数少、训练快、效果好。
Applying Quantum Autoencoders for Time Series Anomaly Detection
- 用量子自编码器重构时间序列，通过重建误差和潜在表示识别异常。
- 在多个数据集上表现优于经典自编码器，参数少60-230倍，训练迭代少5倍。
- 首次在真实量子硬件上实现，性能与模拟结果相当，适合量子机器学习初探者。
异常检测在欺诈识别、模式识别和医学诊断等领域具有重要意义。尽管已有多种经典计算方法，但将量子计算应用于时间序列异常检测仍属未充分探索的领域。本文研究了量子自编码器在时间序列异常检测中的应用，提出两种主要判别方法：（1）分析量子自编码器生成的重建误差；（2）潜空间表示分析。在多种电路结构（ansatz）下的仿真实验表明，量子自编码器在多个数据集上均显著优于经典深度学习自编码器，实现更优异常检测性能的同时，参数量减少60至230倍，训练迭代次数减少5倍。此外，我们已在真实量子硬件上实现了量子编码器，实验结果表明其异常检测性能与模拟结果相当。
原文摘要 · Abstract (English)
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.
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