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Quantum Algorithm for Unsupervised Anomaly Detection

Wenmin Li, Mingchao Guo, Shi‐Jie Pan +5 more

Jan 1, 20230 citations

Quantum Algorithms for Anomaly Detection Using Amplitude Estimation

Mingchao Guo, Hailing Liu, Yongmei Li +4 more

arXiv:2109.13820Physica A Statistical Mechanics and its ApplicationsJan 1, 202239 citations

Quantum Machine Learning for Anomaly Detection in Consumer Electronics

Sounak Bhowmik, Himanshu Thapliyal

Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing world, with frequently emerging new types of anomalies, classical machine learning models are insufficient to prevent all the threats. Quantum Machine Learning (QML) is emerging as a powerful computational tool that can detect anomalies more efficiently. In this work, we have introduced QML and its applications for anomaly detection in consumer electronics. We have shown a generic framework for applying QML algorithms in anomaly detection tasks. We have also briefly discussed popular supervised, unsupervised, and reinforcement learning-based QML algorithms and included five case studies of recent works to show their applications in anomaly detection in the consumer electronics field.Anomaly detection is a crucial task in cyber security. Technological advancement brings new cyber-physical threats like network intrusion, financial fraud, identity theft, and property invasion. In the rapidly changing world, with frequently emerging new types of anomalies, classical machine learning models are insufficient to prevent all the threats. Quantum Machine Learning (QML) is emerging as a powerful computational tool that can detect anomalies more efficiently. In this work, we have introduced QML and its applications for anomaly detection in consumer electronics. We have shown a generic framework for applying QML algorithms in anomaly detection tasks. We have also briefly discussed popular supervised, unsupervised, and reinforcement learning-based QML algorithms and included five case studies of recent works to show their applications in anomaly detection in the consumer electronics field.

arXiv:2409.00294IEEE Computer Society Annual Symposium on VLSIJul 1, 202410 citations

Quantum similarity learning for anomaly detection

Hamed Ali Hammad AHMED, Mihoko NOJIRI, Masahito Yamazaki

Jan 1, 20250 citations

On the Issue of an Anomaly Detection Algorithm for Identifying Potentially Generated Entangled Photons

Sergey Viktorovich Alekseyenko, Dmytro Babets, Kamil Wereszczyński +1 more

Detecting entangled photons is critical for quantum technologies but challenging due to weak signals, noise, and rarity. This study proposes a fast, unsupervised anomaly detection algorithm using a multivariate Gaussian distribution to identify potential entangled photon events. Applied to data from spontaneous parametric down-conversion (SPDC) and silicon photomultiplier (SiMP) detectors, it successfully detected rare correlated events, offering a simple, efficient tool for quantum optics.

International Conference on Control, Decision and Information Technologies (CoDIT)Jul 15, 20250 citations

Adiabatic Quantum Anomaly Detection and Machine Learning

Kristen L. Pudenz, Daniel A. Lidar

Bulletin of the American Physical SocietyFeb 29, 20120 citations

Quantum algorithm for unsupervised anomaly detection

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