发表机构
University of Florence; Eni S.p.A.（佛罗伦萨大学; 埃尼公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对小样本不平衡数据上的异常检测难题，提出两步混合经典-量子架构，在大型光伏电站场景中实现高泛化能力与竞争性精度。
AI 中文摘要
在小型且不平衡的数据集上进行异常检测在机器学习中仍然非常具有挑战性，尽管这种情况在医疗保健、网络安全、金融和能源等多个领域都很常见。数据增强和生成式人工智能可能缓解训练数据稀缺的问题，但它们往往效果不佳，因为异常事件按定义是与高概率正常数据相比不可预测、罕见且高度多样的事件。对伪异常的过拟合、模型崩溃、高维数据、不可解释的黑箱模型以及验证挑战是限制其实际适用性的典型问题。在此背景下，量子机器学习可能提供一条有前景且更可持续的途径，因为它能够以更少的可训练参数和更小的数据集实现更具可解释性的模型，并可在节能的量子硬件上实现。在此，我们提出了一种新颖的两步混合经典-量子架构用于序列数据，并在全球能源转型领域的现实场景中进行了测试，即大型光伏电站的自动化异常检测。所实现的泛化能力和具有竞争力的预测精度可能为新的混合学习模型铺平道路，这些模型能够利用云上可用且更可持续的量子加速器与更传统的耗能高性能计算资源集成后不断增强的计算能力。
英文摘要
Anomaly detection on small and unbalanced datasets remains very challenging in machine learning, although this scenario is common in several domains, including healthcare, cybersecurity, finance, and energy. Data augmentation and generative AI may mitigate training-data scarcity, but they often fall short because anomalies are, by definition, unpredictable, rare, and highly diverse events compared to high-probability normal data. Overfitting to pseudo-anomalies, model collapse, high-dimensional data, uninterpretable black-box models, and validation challenges are typical issues limiting their practical applicability. In this context, quantum machine learning may provide a promising and more sustainable avenue because it can enable more interpretable models with far fewer trainable parameters and smaller datasets, implementable on energy-efficient quantum hardware. Here, we propose a novel two-step hybrid classical--quantum architecture for sequential data and test it on a realistic scenario in the global energy-transition domain, i.e., automated anomaly detection in large-scale photovoltaic plants. The achieved generalization capability and competitive prediction accuracy may pave the way for new hybrid learning models able to exploit the continuously increasing power of cloud-available and more sustainable quantum accelerators integrated with more traditional energy-hungry High Performance Computing resources.
