Open set anomaly detection (OSAD) is a crucial task that aims to identify abnormal patterns or behaviors in data sets, especially when the anomalies observed during training do not represent all possible classes of anomalies. The recent advances in quantum computing in handling complex data structures and improving machine learning models herald a paradigm shift in anomaly detection methodologies. This study proposes a Quantum Scoring Module (Qsco), embedding quantum variational circuits into neural networks to enhance the model's processing capabilities in handling uncertainty and unlabeled data. Extensive experiments conducted across eight real-world anomaly detection datasets demonstrate our model's superior performance in detecting anomalies across varied settings and reveal that integrating quantum simulators does not result in prohibitive time complexities. Our study validates the feasibility of quantum-enhanced anomaly detection methods in practical applications.

翻译：开放集异常检测（OSAD）是一项关键任务，旨在识别数据集中的异常模式或行为，尤其是在训练期间观察到的异常并不代表所有可能的异常类别时。量子计算在处理复杂数据结构和改进机器学习模型方面的最新进展预示着异常检测方法的范式转变。本研究提出一种量子评分模块（Qsco），将量子变分电路嵌入神经网络，以增强模型在处理不确定性和未标记数据时的处理能力。在八个真实世界异常检测数据集上进行的大量实验表明，我们的模型在不同设置下检测异常方面具有优越性能，并揭示集成量子模拟器不会导致难以承受的时间复杂度。我们的研究验证了量子增强异常检测方法在实际应用中的可行性。