Neural quantum support vector data description for one-class classification
One-class classification (OCC) is a fundamental problem in machine learning with numerous applications, such as anomaly detection and quality control. With the increasing complexity and dimensionality of modern datasets, there is a growing demand for advanced OCC techniques with better expressivity and efficiency. We introduce Neural Quantum Support Vector Data Description (NQSVDD), a classical-quantum hybrid framework for OCC that performs end-to-end optimized hierarchical representation learning. NQSVDD integrates a classical neural network with trainable quantum data encoding and a variational quantum circuit, enabling the model to learn nonlinear feature transformations tailored to th...
| Authors | Changjae Im, Hyeondo Oh, Daniel K. Park |
|---|---|
| Primary category | quant-ph |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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