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arXiv · 2504.13113

Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders

Jason Zev Ludmir·Sophia Rebello·Jacob Ruiz·Tirthak Patel
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Abstract

Detecting mission-critical anomalous events and data is a crucial challenge across various industries, including finance, healthcare, and energy. Quantum computing has recently emerged as a powerful tool for tackling several machine learning tasks, but training quantum machine learning models remains challenging, particularly due to the difficulty of gradient calculation. The challenge is even greater for anomaly detection, where unsupervised learning methods are essential to ensure practical applicability. To address these issues, we propose Quorum, the first quantum anomaly detection framework designed for unsupervised learning that operates without requiring any training.

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Jason Zev Ludmir, Sophia Rebello, Jacob Ruiz, Tirthak Patel. 2025-04-17. Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders. https://arxiv.org/abs/2504.13113

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