用量子启发张量网络实现高效异常检测，模型参数线性增长。
SMT-AD: a scalable quantum-inspired anomaly detection approach
- 基于多分辨率张量叠加与傅里叶嵌入，构建可并行的异常检测模型
- 参数量随特征数线性增长，在信用卡数据上性能媲美主流方法
- 能自动聚焦关键特征，轻松压缩模型且可能提升效果
量子启发张量网络算法在机器学习任务中展现出高效性，尤其适用于异常检测。本文提出一种高度可并行化的量子启发方法 SMT-AD（Superposition of Multiresolution Tensors for Anomaly Detection），其核心是将键维数为1的矩阵积算子进行叠加，并结合傅里叶辅助特征嵌入对输入数据进行变换。模型可学习参数数量与特征维度、嵌入分辨率及矩阵积算子结构中的附加组件数量呈线性关系。在标准数据集（如信用卡交易）上的实验表明，即使采用极简配置，该方法仍能取得与现有主流异常检测基准相当的性能。此外，该方法可直接通过突出最相关输入特征来降低模型权重，甚至进一步提升性能。
原文摘要 · Abstract (English)
Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection. Here, we propose a highly parallelizable quantum-inspired approach which we call SMT-AD from Superposition of Multiresolution Tensors for Anomaly Detection. It is based upon the superposition of bond-dimension-1 matrix product operators to transform the input data with Fourier-assisted feature embedding, where the number of learnable parameters grows linearly with feature size, embedding resolutions, and the number of additional components in the matrix product operators structure. We demonstrate successful anomaly detection when applied to standard datasets, including credit card transactions, and find that, even with minimal configurations, it achieves competitive performance against established anomaly detection baselines. Furthermore, it provides a straightforward way to reduce the weight of the model and even improve the performance by highlighting the most relevant input features.
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