Neural quantum support vector data description for one-class classification

• URL: http://arxiv.org/abs/2603.02700v1
• Date: Tue, 03 Mar 2026 07:45:32 GMT
• Title: Neural quantum support vector data description for one-class classification
• Authors: Changjae Im, Hyeondo Oh, Daniel K. Park,
• Abstract summary: One-class classification (OCC) is a fundamental problem in machine learning with numerous applications.<n>We introduce Neural Quantum Support Vector Data Description (NQSVDD), a classical-quantum hybrid framework for OCC.<n>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.
• Score: 0.19116784879310023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Quantum LEGO Learning: A Modular Design Principle for Hybrid Artificial Intelligence [63.39968536637762]
  We introduce Quantum LEGO Learning, a learning framework that treats classical and quantum components as reusable, composable learning blocks.<n>Within this framework, a pre-trained classical neural network serves as a frozen feature block, while a VQC acts as a trainable adaptive module.<n>We develop a block-wise generalization theory that decomposes learning error into approximation and estimation components.
  arXiv  Detail & Related papers  (2026-01-29T14:29:21Z)
• Quantum AI for Cybersecurity: A hybrid Quantum-Classical models for attack path analysis [1.2676356746752895]
  This study investigates the potential of hybrid quantum-classical learning to enhance feature representations for intrusion detection.<n>We show that quantum-enhanced representations demonstrate superior attack recall and improved class separability when data is scarce.<n>Results highlight the potential of quantum embeddings to improve generalization and representation quality in cybersecurity tasks.
  arXiv  Detail & Related papers  (2026-01-05T16:11:39Z)
• Parallel Multi-Circuit Quantum Feature Fusion in Hybrid Quantum-Classical Convolutional Neural Networks for Breast Tumor Classification [0.0]
  We present a hybrid Quantum-Classical Convolutional Neural Network (QCNN) architecture designed for the binary classification of the BreastMNIST dataset.<n>Our results indicate that hybrid QCNN architectures can leverage entanglement and quantum feature fusion to enhance medical image classification tasks.
  arXiv  Detail & Related papers  (2025-11-29T17:47:14Z)
• Towards Quantum Enhanced Adversarial Robustness with Rydberg Reservoir Learning [45.92935470813908]
  Quantum computing reservoir (QRC) leverages the high-dimensional, nonlinear dynamics inherent in quantum many-body systems.<n>Recent studies indicate that perturbation quantums based on variational circuits remain susceptible to adversarials.<n>We investigate the first systematic evaluation of adversarial robustness in a QR based learning model.
  arXiv  Detail & Related papers  (2025-10-15T12:17:23Z)
• Selective Feature Re-Encoded Quantum Convolutional Neural Network with Joint Optimization for Image Classification [3.8876018618878585]
  Quantum convolutional neural networks (QCNNs) have demonstrated promising results in classifying both quantum and classical data.<n>This study proposes a novel strategy to enhance feature processing and a QCNN architecture for improved classification accuracy.
  arXiv  Detail & Related papers  (2025-07-02T18:51:56Z)
• VQC-MLPNet: An Unconventional Hybrid Quantum-Classical Architecture for Scalable and Robust Quantum Machine Learning [50.95799256262098]
  Variational quantum circuits (VQCs) hold promise for quantum machine learning but face challenges in expressivity, trainability, and noise resilience.<n>We propose VQC-MLPNet, a hybrid architecture where a VQC generates the first-layer weights of a classical multilayer perceptron during training, while inference is performed entirely classically.
  arXiv  Detail & Related papers  (2025-06-12T01:38:15Z)
• Quantum Pointwise Convolution: A Flexible and Scalable Approach for Neural Network Enhancement [0.0]
  We propose a novel architecture, which incorporates pointwise convolution within a quantum neural network framework.<n>By using quantum circuits, we map data to a higher-dimensional space, capturing more complex feature relationships.<n>In experiments, we applied the quantum pointwise convolution layer to classification tasks on the FashionMNIST and CIFAR10 datasets.
  arXiv  Detail & Related papers  (2024-12-02T08:03:59Z)
• Quantum Kernel-Based Long Short-term Memory [0.30723404270319693]
  We introduce the Quantum Kernel-Based Long Short-Term Memory (QK-LSTM) network to capture complex, non-linear patterns in sequential data. This quantum-enhanced architecture demonstrates efficient convergence, robust loss minimization, and model compactness. Benchmark comparisons reveal that QK-LSTM achieves performance on par with classical LSTM models, yet with fewer parameters.
  arXiv  Detail & Related papers  (2024-11-20T11:39:30Z)
• ClusterQ: Semantic Feature Distribution Alignment for Data-Free Quantization [111.12063632743013]
  We propose a new and effective data-free quantization method termed ClusterQ. To obtain high inter-class separability of semantic features, we cluster and align the feature distribution statistics. We also incorporate the intra-class variance to solve class-wise mode collapse.
  arXiv  Detail & Related papers  (2022-04-30T06:58:56Z)
• When BERT Meets Quantum Temporal Convolution Learning for Text Classification in Heterogeneous Computing [75.75419308975746]
  This work proposes a vertical federated learning architecture based on variational quantum circuits to demonstrate the competitive performance of a quantum-enhanced pre-trained BERT model for text classification. Our experiments on intent classification show that our proposed BERT-QTC model attains competitive experimental results in the Snips and ATIS spoken language datasets.
  arXiv  Detail & Related papers  (2022-02-17T09:55:21Z)
• Quantum Machine Learning with SQUID [64.53556573827525]
  We present the Scaled QUantum IDentifier (SQUID), an open-source framework for exploring hybrid Quantum-Classical algorithms for classification problems. We provide examples of using SQUID in a standard binary classification problem from the popular MNIST dataset.
  arXiv  Detail & Related papers  (2021-04-30T21:34:11Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.