[2603.02700] Neural quantum support vector data description for one-class classification

Quantum Physics arXiv:2603.02700 (quant-ph) [Submitted on 3 Mar 2026] Title:Neural quantum support vector data description for one-class classification Authors:Changjae Im, Hyeondo Oh, Daniel K. Park View a PDF of the paper titled Neural quantum support vector data description for one-class classification, by Changjae Im and 2 other authors View PDF HTML (experimental) Abstract: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 the OCC objective. The hybrid architecture maps input data into an intermediate high-dimensional feature space and subsequently projects it into a compact latent space defined through quantum measurements. Importantly, both the feature embedding and the latent representation are jointly optimized such that normal data form a compact cluster, for which a minimum-volume enclosing hypersphere provides an effective decisi...