Jul 2026· Anais do I Simpósio Brasileiro de Computação e Comunicação Quânticas (SBCCQ 2026)· pp. 107-118· 0 citations· 23 references
TL;DR
Overall, quantum and hybrid autoencoders are not universally superior, but deliver competitive anomaly detection with remarkably high parametric efficiency.
Abstract
This work investigates variational quantum autoencoders (QAE) for one-class anomaly detection under strict parametric constraints, shifting the evaluation focus from absolute performance to performance–capacity tradeoffs. We implement a pure QAE baseline and a Hybrid QAE with classical compression/decoding around a quantum latent block, comparing both against classical baselines - AE, VAE, Isolation Forest, and One-Class SVM - on the NSL-KDD and ECG5000 benchmarks. The central contribution is a fairnessoriented evaluation protocol combining standard detection metrics (AUC-ROC, AUC-PR, and F1) with a performance-density measure (AUC-ROC per 1,000 trainable parameters) and a fair-budget comparison against a compact classical AE. On NSL-KDD, the QAE baseline achieves 0.9612 AUC-ROC with only 476 parameters versus 0.9679 for a classical AE with 5,580 parameters, while attaining 2.0193 AUC-ROC/1k parameters against 0.1735 for the classical AE - an order-of-magnitude improvement in parametric efficiency. Under a fairbudget setting, the QAE baseline also surpasses the compact classical AE on NSL-KDD (0.9612 vs. 0.9405). In contrast, ECG5000 favors classical methods, indicating domain dependence rather than universal quantum advantage. Overall, quantum and hybrid autoencoders are not universally superior, but deliver competitive anomaly detection with remarkably high parametric efficiency.
We developed a novel workflow that leverages Quantum Kernel feature space expressiveness combined with classical dimensionality reduction techniques. This workflow enables detection and visual identification of multiple simultaneous anomalies using acoustic data from a single non-contact sensor. Our newly developed method provides an intuitive interface for operators to identify specific anomalies. By combining conventional Mel-frequency cepstral coefficients (MFCC) with principal component analysis (PCA), the newly constructed Quantum Kernel achieves near-perfect classification (F1 = 1.0 under specific conditions: j ≥ 9 features, file-level data splits) of complex multi-source anomalies, significantly outperforming the classical RBF kernel (F1 ≈ 0.75-0.76) and 1D-CNN autoencoder baseline (F1 ≈ 0.60-0.85). The Quantum Kernel's superior representational power enables accurate anomaly detection with minimal training data. Our results demonstrate simulation-based evidence of potential quantum advantage under ideal conditions, where the performance of our method dramatically exceeds that of classical approaches as the number of features increases, pending validation on noisy intermediate-scale quantum (NISQ) hardware. This hybrid quantum-classical machine learning approach demonstrates significant potential for industrial applications, particularly for complex time-series data in data-scarce regimes where classical methods exhibit limited performance.
This paper presents a comparative benchmarking study of classical and quantum machine learning models for intrusion detection using three benchmark datasets: NSL-KDD, UNSW-NB15, and MQTTEEB-D2025. The study evaluates how preprocessing choices, feature selection strategies, and quantum encoding methods influence model performance across datasets with different levels of noise and complexity. A unified pipeline is adopted, incorporating normalization, imbalance handling, dimensionality reduction, and two feature selection approaches: Random Forest importance and a quantum-aware method based on Quantum Kernel Alignment with Mutual Information. Four models are assessed: Support Vector Machine, Random Forest, Quantum Support Vector Machine, and Pegasos Quantum SVM. Results show that classical models remain stable across datasets, while quantum models are more sensitive to feature representation and kernel alignment. Quantum performance improves significantly with quantum-aware feature selection, particularly on cleaner datasets, whereas heterogeneous datasets remain challenging. Pegasos Quantum SVM offers a favorable balance between accuracy and computational efficiency, highlighting the importance of preprocessing alignment for practical quantum intrusion detection.
Taha M. Mahmoud, N. Kaabouch· 2026 6th International Confe...· 0 citations
This paper investigates whether quantum principal component analysis can provide useful features for IDS without relying on claims of end-to-end quantum superiority, and finds that QPCA is most useful as a representation enhancer under NISQ-compatible, not hardware-validated, constraints.
Murilo Salem, D. Pontes, Luísa Böhm et al.· Anais do I Simpósio Brasilei...· 0 citations
Next-generation HQCNN architectures, including DMERA, HEA–QCNN, and (bQCNN) designs, significantly optimized for NISQ hardware are presented, establishing a scalable, parameter-efficient framework for high-performance hybrid quantum-classical ML on near-term devices.
Shyam R. Sihare, A. Cherukuri· SN Computer Science· 0 citations
Tabular anomaly detection is dominated by classical density-proxy methods (Isolation Forest, OCSVM, LOF), reconstruction-based detectors (Autoencoders, VAEs), and modern non-parametric scorers (COPOD, ECOD, Deep SVDD), all of which approximate the inlier distribution only indirectly; explicit energy-based models are largely absent. Motivated by the recent revival of EBMs in deep learning (e.g., Energy-Based Transformers, JEPA), we revisit the classical Deep Boltzmann Machine (DBM) for this task and hypothesize that its mean-field energy combines more effectively with a reconstruction-based score than same-lineage pairs do. We evaluate a two-hidden-layer DBM on two tabular benchmarks spanning distinct domains (UCI Bank Marketing and NSL-KDD) against eight classical and modern baselines across twenty random seeds. The DBM mean-field energy matches the strongest baseline (the Autoencoder) on Bank Marketing and statistically beats it on NSL-KDD, while significantly outperforming the remaining seven on both datasets. When fused with the Autoencoder via rank fusion, the DBM energy yields a statistically significant improvement on both datasets (AUROC=+0.014, p<0.01 on Bank Marketing; +0.002, p<0.001 on NSL-KDD); every non-DBM-derived base model instead fails to improve or significantly degrades the AE-paired ensemble. Our position is that classical EBMs, exemplified by the DBM, deserve a place in the tabular anomaly detection toolbox as a non-redundant complementary view to the reconstruction-based scores that dominate current practice.
A core task in quantum anomaly detection is to compute an anomaly score that quantifies how strongly a test quantum state deviates from a given quantum dataset assumed to be normal. Classically, principal component analysis (PCA) for centered data computes the anomaly score by evaluating the test sample relative to the subspace spanned by the selected leading eigenvectors. However, for quantum data that lack a standard centering, explicitly recovering principal eigenvectors, constructing full Gram matrices, or loading quantum-random-access-memory-style data can be more costly than estimating the anomaly score itself. To avoid these costs, we propose Quantum Spectral Anomaly Detection (QSPADE), which computes PCA-like anomaly scores directly from the spectrum of the average state of the normal dataset. By replacing hard PCA rank selection with a smooth, temperature-controlled spectral threshold, QSPADE makes near-threshold spectral components contribute partially to the anomaly score. This makes the score vary continuously rather than jump when a borderline component is included or excluded, and makes it less sensitive to noise or arbitrary hard cutoffs near the threshold. In the zero-temperature limit, QSPADE recovers the hard-projector PCA score. The proposed measurement-based quantum detector can be calibrated with a sample complexity independent of the data dimension. Numerical simulations show that QSPADE behaves like kernel-PCA on encoded classical data and detects changes across a transverse-field Ising transition without predefined order parameters. Consequently, QSPADE gives an efficient framework for both quantum-kernel anomaly detection on encoded classical data and the monitoring of quantum-native systems where diagnostic observables are unknown.
Yewei Yuan, Michele Minervini, Mark M. Wilde et al.· 1 citation