Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders

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 remain...

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Veröffentlicht in:2025 62nd ACM/IEEE Design Automation Conference (DAC) S. 1 - 7
Hauptverfasser: Ludmir, Jason Zev, Rebello, Sophia, Ruiz, Jacob, Patel, Tirthak
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 22.06.2025
Schlagworte:
Anomaly detection
Machine learning
Medical services
Mission critical systems
Noise
Quantum computing
Quantum system
Resilience
Training
Unsupervised learning
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Zusammenfassung: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.
DOI:10.1109/DAC63849.2025.11132860