Anomaly Detection (AD) defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing incidents such as system misconfigurations, malware infections, or cyberattacks. In enterprise environments like SAP HANA Cloud systems, this task often involves monitoring high-dimensional, multivariate time series (MTS) derived from telemetry and log data. With the advent of quantum machine learning offering efficient calculations in high-dimensional latent spaces, many avenues open for dealing with such complex data. One approach is the Quantum Autoencoder (QAE), an emerging and promising method with potential for application in both data compression and AD. However, prior applications of QAEs to time series AD have been restricted to univariate data, limiting their relevance for real-world enterprise systems. In this work, we introduce a novel QAE-based framework designed specifically for MTS AD towards enterprise scale. We theoretically develop and experimentally validate the architecture, demonstrating that our QAE achieves performance competitive with neural-network-based autoencoders while requiring fewer trainable parameters. We evaluate our model on datasets that closely reflect SAP system telemetry and show that the proposed QAE is a viable and efficient alternative for semisupervised AD in real-world enterprise settings.

翻译：异常检测（AD）定义为识别偏离典型或正常模式的观测值或事件的任务，这是IT安全领域识别系统配置错误、恶意软件感染或网络攻击等事件的关键能力。在SAP HANA云系统等企业环境中，该任务通常涉及监测源自遥测和日志数据的高维多元时间序列（MTS）。随着量子机器学习在高维潜在空间中提供高效计算的可能性，处理此类复杂数据的多种途径得以开辟。量子自编码器（QAE）作为一种新兴且极具前景的方法，在数据压缩和异常检测领域均具有应用潜力。然而，此前QAE在时间序列异常检测中的应用仅限于单变量数据，限制了其在真实企业系统中的相关性。本研究提出一种专为企业级多元时间序列异常检测设计的新型QAE框架。我们从理论上发展并实验验证了该架构，证明所提出的QAE在需更少可训练参数的同时，可实现与基于神经网络的自动编码器相媲美的性能。我们在紧密反映SAP系统遥测特性的数据集上评估模型，结果表明该QAE是真实企业场景中半监督异常检测的可行且高效替代方案。