用于异常检测的量子支持向量数据描述

IF 6.3 2区 物理与天体物理 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Machine Learning Science and Technology Pub Date : 2024-08-21 DOI:10.1088/2632-2153/ad6be8
Hyeondo Oh, Daniel K Park
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引用次数: 0

摘要

异常检测是数据分析和模式识别中的一个关键问题,在各个领域都有应用。我们介绍了量子支持向量数据描述(QSVDD),这是一种专为异常检测设计的无监督学习算法。QSVDD 利用浅深度量子电路来学习一个最小体积的超球,该超球紧紧包裹着正常数据,专为噪声中等规模量子计算(NISQ)的限制而量身定制。MNIST 和时尚 MNIST 图像数据集以及信用卡欺诈检测的仿真结果表明,在类似的训练条件下,QSVDD 的性能优于量子自动编码器和基于深度学习的方法。值得注意的是,QSVDD 只需要极少量的模型参数,这些参数随输入量子比特数量的增加而呈对数增长。这样就能通过简单的训练环境实现高效学习,从而为异常检测提供了一个性能强大的紧凑型量子机器学习模型。
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Quantum support vector data description for anomaly detection
Anomaly detection is a critical problem in data analysis and pattern recognition, finding applications in various domains. We introduce quantum support vector data description (QSVDD), an unsupervised learning algorithm designed for anomaly detection. QSVDD utilizes a shallow-depth quantum circuit to learn a minimum-volume hypersphere that tightly encloses normal data, tailored for the constraints of noisy intermediate-scale quantum (NISQ) computing. Simulation results on the MNIST and Fashion MNIST image datasets, as well as credit card fraud detection, demonstrate that QSVDD outperforms both quantum autoencoder and deep learning-based approaches under similar training conditions. Notably, QSVDD requires an extremely small number of model parameters, which increases logarithmically with the number of input qubits. This enables efficient learning with a simple training landscape, presenting a compact quantum machine learning model with strong performance for anomaly detection.
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来源期刊
Machine Learning Science and Technology
Machine Learning Science and Technology Computer Science-Artificial Intelligence
CiteScore
9.10
自引率
4.40%
发文量
86
审稿时长
5 weeks
期刊介绍: Machine Learning Science and Technology is a multidisciplinary open access journal that bridges the application of machine learning across the sciences with advances in machine learning methods and theory as motivated by physical insights. Specifically, articles must fall into one of the following categories: advance the state of machine learning-driven applications in the sciences or make conceptual, methodological or theoretical advances in machine learning with applications to, inspiration from, or motivated by scientific problems.
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