Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum Autoencoders
This work addresses the problem of detecting anomalous events in industries like finance and healthcare, but it is incremental as it builds on existing quantum computing concepts without introducing a new paradigm.
The authors tackled the challenge of unsupervised anomaly detection in mission-critical industries by proposing Quorum, a quantum anomaly detection framework that operates without any training, achieving a 15% improvement in detection accuracy over classical baselines on synthetic datasets.
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.