arXiv Machine Learning
Jul 23

Classical Hardware Acceleration of Quantum Autoencoders for Real-Time Anomaly Detection in Collider Experiments

arXiv:2607. 20302v1 Announce Type: new Abstract: Quantum machine learning (QML) algorithms in high energy physics (HEP) can efficiently represent and leverage long-range, high-order correlations in high-dimensional collider data, potentially with fewer parameters and favorable scaling relative to classical models.

By Ivan Ge, Sagar Addepalli, Abhilasha Dave, Julia Gonski
arXiv Machine Learning
Jul 7

Quantum Spectral Anomaly Detection

arXiv:2607. 05307v1 Announce Type: cross Abstract: A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal.

By Yewei Yuan, Michele Minervini, Mark M. Wilde, Nana Liu
arXiv Machine Learning
Sep 23

Federating Quantum and Classical Computing: A Privacy-Preserving Hybrid Approach

arXiv:2609.25082v1 Announce Type: new Abstract: Quantum machine learning (QML) is increasingly recognized as one of the most promising near-term applications of quantum computing, viewed as a next-fr...

By Carlos Cano, Daniel M. Jimenez-Gutierrez, Diego Sal, Georgios Kellaris, Joaquin del Rio, Oleksii Sliusarenko, Xabi Uribe-Etxebarria
arXiv Machine Learning
Jun 19

SMT-AD: a scalable quantum-inspired anomaly detection approach

arXiv:2604. 06265v2 Announce Type: replace Abstract: Quantum-inspired tensor networks algorithms have shown to be effective and efficient models for machine learning tasks, including anomaly detection.

By Apimuk Sornsaeng, Si Min Chan, Wenxuan Zhang, Swee Liang Wong, Joshua Lim, Jonathan Pan, Dario Poletti