SMT-AD：一种可扩展的量子启发式异常检测方法
SMT-AD: a scalable quantum-inspired anomaly detection approach
AI总结：
提出基于多分辨率张量叠加的量子启发式异常检测方法SMT-AD，通过傅里叶辅助特征嵌入和矩阵乘积算子实现线性可扩展，在标准数据集上取得竞争性能。
摘要：
量子启发的张量网络算法已被证明是机器学习任务（包括异常检测）中有效且高效的模型。在此，我们提出一种高度可并行化的量子启发式方法，称为SMT-AD（Superposition of Multiresolution Tensors for Anomaly Detection）。它基于键维数为1的矩阵乘积算子的叠加，通过傅里叶辅助特征嵌入对输入数据进行变换，其中可学习参数的数量随特征大小、嵌入分辨率和矩阵乘积算子结构中附加组件的数量线性增长。我们展示了在标准数据集（包括信用卡交易）上成功的异常检测，并发现即使采用最小配置，它也能与已建立的异常检测基线相媲美。此外，它提供了一种直接的方法来减少模型权重，甚至通过突出最相关的输入特征来提高性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
