quantum-autoencoder-general-anomaly

name	quantum-autoencoder-general-anomaly
description	Quantum convolutional autoencoder (QCAE) for reconstruction-based anomaly detection using QCNN architectures - semi-supervised training on normal samples with reconstruction error as anomaly score.
category	quantum
created	2026-07-06T00:00:00.000Z
source	arXiv:2607.02135

Quantum Convolutional Autoencoders for Anomaly Detection

Source

arXiv:2607.02135 - "Quantum Convolutional Autoencoders for Reconstruction-Based Anomaly Detection" by Donovan Slabbert, Francesco Petruccione (2026-07-02)

Overview

Quantum convolutional neural networks (QCNNs) adapted into quantum autoencoder (QAE) framework for reconstruction-based anomaly detection. Models trained semi-supervised on normal samples with reconstruction error as anomaly score.

Core Methodology

1. QCNN-to-QAE Architecture: Adapt quantum convolutional neural network architecture into autoencoder framework. Hierarchical representation of quantum information enables efficient compression.

2. Two Architecture Variants: Models differ in treatment of latent information:

   • Architecture A: Direct latent encoding
   • Architecture B: Modified latent space handling

3. Semi-Supervised Training: Train on normal samples only to reconstruct feature-extracted and dimensionally reduced time-series data.

4. Reconstruction Error Scoring: Use reconstruction error as anomaly score - anomalies resist compression, producing higher reconstruction errors.

Key Findings

• QCNNs provide efficient parameterization for anomaly detection
• Hierarchical quantum representations capture data structure effectively
• Semi-supervised approach works well with limited normal data
• Two architecture variants offer different tradeoffs
• Applicable to scientific data analysis across domains

Applications

• Anomaly detection in time-series data
• Scientific data analysis
• Quantum machine learning pipelines
• Semi-supervised classification tasks
• Dimensionality reduction for anomaly detection

Trigger Words

quantum convolutional autoencoder, anomaly detection, QCNN, semi-supervised, reconstruction error, time-series analysis, quantum machine learning

Activation

When:

• Building anomaly detection systems with quantum circuits
• Working with quantum convolutional architectures
• Designing semi-supervised learning pipelines
• Analyzing time-series data for anomalies
• Exploring quantum advantage in unsupervised learning