Neural quantum support vector data description for one-class classification

TL;DR

This paper introduces NQSVDD, a hybrid classical-quantum framework for one-class classification that enhances expressivity and efficiency through end-to-end hierarchical learning, outperforming classical methods on benchmark datasets.

Contribution

The paper presents a novel classical-quantum hybrid model for OCC that jointly optimizes feature and latent representations using quantum encoding and variational circuits.

Findings

Achieves competitive or superior AUC performance compared to classical Deep SVDD.

Maintains parameter efficiency and robustness under realistic noise conditions.

Demonstrates effectiveness on benchmark datasets.

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 decision boundary. Experimental evaluations on benchmark datasets demonstrate that NQSVDD achieves competitive or superior AUC performance compared to classical Deep SVDD and quantum baselines, while maintaining parameter efficiency and robustness under realistic noise conditions.