We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data. The approach leverages angle encoding to map image patches into quantum states, followed by a variational encoder-decoder architecture trained to discard information via auxiliary trash qubits. Anomaly scores reflect the degree to which inputs resist compression relative to normal data, with higher scores corresponding to deviations from the learned normal manifold. Evaluated on publicly available brain MRI DICOM datasets, the method achieves a slice-level ROC-AUC of approximately 0.95 and a patch-level ROC-AUC of approximately 0.813, outperforming classical autoencoder and PCA baselines. Analysis of the learned parameters reveals a pronounced encoder-decoder asymmetry, where effective anomaly detection arises from structured information compression within the encoder rather than increased parameter magnitude or decoder expressivity. This results in a controlled compression-reconstruction trade-off with a clear operating regime that supports principled threshold selection. Qualitative evaluation further shows that the QAE produces spatially localized anomaly heatmaps aligned with tumorous regions. The results, supported by promising baseline performances, demonstrate that quantum autoencoders provide an interpretable and controllable mechanism for anomaly detection based on incompressibility with respect to a learned latent representation. This work highlights the potential of quantum autoencoders as a principled tool for studying compression dynamics in quantum machine learning, with promising implications for decision support in medical imaging workflows.
We study a quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI data. The approach leverages angle encoding to map image patches into quantum states, followed by a variational encoder-decoder architecture trained to discard information via auxiliary trash qubits. Anomaly scores reflect the degree to which inputs resist compression relative to normal data, with higher scores corresponding to deviations from the learned normal manifold. Evaluated on publicly available brain MRI DICOM datasets, the method achieves a slice-level ROC-AUC of approximately 0.95 and a patch-level ROC-AUC of approximately 0.813, outperforming classical autoencoder and PCA baselines. Analysis of the learned parameters reveals a pronounced encoder-decoder asymmetry, where effective anomaly detection arises from structured information compression within the encoder rather than increased parameter magnitude or decoder expressivity. This results in a controlled compression-reconstruction trade-off with a clear operating regime that supports principled threshold selection. Qualitative evaluation further shows that the QAE produces spatially localized anomaly heatmaps aligned with tumorous regions. The results, supported by promising baseline performances, demonstrate that quantum autoencoders provide an interpretable and controllable mechanism for anomaly detection based on incompressibility with respect to a learned latent representation. This work highlights the potential of quantum autoencoders as a principled tool for studying compression dynamics in quantum machine learning, with promising implications for decision support in medical imaging workflows.
Quantum autoencoder (QAE) is a key tool in quantum machine learning and can be applied to areas such as dimensionality reduction and feature extraction [1]. Anomaly detection identifies deviations from normal patterns, which is crucial yet challenging due to the need to detect risks while minimizing false positives [2]. We propose a QAE-based anomaly detection scheme using an optical quantum neural network. Figure 1 (A) shows the basic structure of the QAE: the encoder compresses the input quantum state into a low-dimensional latent space, and the decoder reconstructs the original quantum state from the latent space. QAE-based anomaly detection works by learning the low-dimensional features of normal data and minimizing its reconstruction error. Normal data can be well reconstructed, while anomalous data exhibits a larger reconstruction error. When the reconstruction error of test data exceeds a set threshold, it is classified as anomalous. We constructed the QAE using an optical quantum neural network consisting of 50:50 beam splitters and controllable phase shifters [3]. Due to the unitary matrix property, only the encoder needs to be trained, and the decoder is simply the conjugate transpose of the encoder. To compress an N-dimensional quantum stateρintoKdimensions, efficient compression can be achieved by minimizing the expectation value of the projection onto the trash state, given byC(θ,ϕ)=Tr(PU(θ,ϕ)ρU+(θ,ϕ))[4]. Here,P=(IK⊕∣0⟩⊗N−K⟨0∣⊗(N−K))represents the projection onto the trash state,IKis the K-dimensional identity matrix,∣0⟩⊗(N−K)isthe trash state,Trdenotes the trace,U(θ,ϕ)is the encoder with adjustable parameters, andU†(θ,ϕ)is the decoder. We only need to train the encoder during the training process, and the decoder is simply its conjugate transpose.
Quantum convolutional neural networks (QCNNs) have become increasingly popular in quantum machine learning (QML) due to their efficient parameterization and hierarchical representation of quantum information. Anomaly detection is an important machine learning task with applications across a wide range of domains, including scientific data analysis. In this work, we adapt a QCNN architecture into a quantum autoencoder (QAE) framework for reconstruction-based anomaly detection. The models are trained in a semi-supervised manner on normal samples to reconstruct feature-extracted and dimensionally reduced time-series data, with reconstruction error used as an anomaly score. We investigate two quantum convolutional autoencoder architectures that differ in their treatment of latent information: a hierarchical architecture in which information remains distributed across the circuit and a bottleneck-based architecture in which information is explicitly compressed and reconstructed using additional decoder qubits. The size of the quantum latent space is varied to study its influence on reconstruction accuracy and anomaly detection performance. The approaches are benchmarked against both a variational quantum circuit and a comparable classical baseline using a real-world exoplanet anomaly-detection dataset. Results indicate a trade-off between latent-space size and model capacity, while also suggesting that explicit latent-space compression through a quantum bottleneck can improve anomaly detection performance relative to architectures that retain information throughout the circuit.
Medical imaging systems generate large volumes of high-resolution images, making efficient storage, transmission, and real-time clinical analysis challenging. This work proposes a Region of Interest (ROI)-Aware Quantum Autoencoder (ROI-QAE) framework to achieve high compression efficiency while preserving diagnostic image quality. The ROI-Aware Quantum Autoencoder (ROI-QAE) is a four-stage pipeline for compressing computed tomography (CT), magnetic resonance imaging (MRI), and COVID-19 chest X-ray images within a single unified implementation. The first stage uses a Vision Transformer (ViT) with eight-head multi-head self-attention over 32×32-pixel patches to generate a binary region-of-interest (ROI) mask, requiring no labelled annotations. The second stage applies an ROI-aware adaptive JPEG engine. The third stage introduces a Quantum Autoencoder (QAE) built in PennyLane that operates on 8×8-pixel patches. A three-layer classical encoder maps each 64-dimensional patch to four qubit angle values, a three-layer Strongly Entangling Layers parameterized quantum circuit (PQC) on a lightning Qubit device processes these angles, and a three-layer classical decoder reconstructs 64-pixel values. The fourth stage applies lossless Huffman entropy coding. Three modality-specific QAE models are trained on 60,000 patches each (300 training images × 200 patches per image) using the Adam optimizer (initial learning rate 5×10⁻³, weight decay 1×10⁻⁵) with a cosine-annealed learning-rate schedule and gradient-norm clipping. Evaluated on 50 held-out test images per modality (drawn from pools of 120 CT, 120 MRI, and 100 X-ray test images), the framework yields mean PSNR of 23.70 dB, 24.73 dB, and 29.18 dB; SSIM of 0.7969, 0.7256, and 0.7396; and space savings of 81.6%, 81.2%, and 80.5% for CT, MRI, and X-ray respectively. Huffman compression ratios reach 1.7304 (CT), 1.3544 (MRI), and 1.1470 (X-Ray). The framework demonstrates that parameterized quantum circuits with as few as 36 quantum parameters can be embedded into a clinically aware compression pipeline and trained on commodity cloud hardware.
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.
The practical value of quantum machine learning for network anomaly detection remains unclear under controlled and comparable benchmarking conditions. This study evaluates a trash-qubit-based Quantum Autoencoder (QAE) against seven classical anomaly-detection methods on UNSW-NB15 using shared data partitions, preprocessing steps, model selection procedures, and threshold calibration protocols. The experimental design included five independent seeds, three training set sizes, two feature budgets, and 12 QAE architectures, together with low-FPR analysis, finite-shot and synthetic-noise evaluation, less-constrained classical reference settings, and a cross-dataset assessment on TON_IoT. The Angle-RY encoding with a Real-Amplitudes Ring ansatz at depth L = 2 achieved the highest mean validation-selection AUPRC. On UNSW-NB15, the QAE was competitive in AUPRC and AUROC but remained behind the strongest classical baselines in F1-score, and its attack recall declined sharply under low-FPR constraints. The TON_IoT results further showed that relative model performance depended on the dataset. Under finite-shot and synthetic-noise conditions, anomaly-score rankings were largely preserved, whereas threshold-based decisions were more sensitive. Overall, the QAE showed competitive continuous-score discrimination on UNSW-NB15, but achieving reliable detection at low false-alarm rates remains a key operational challenge.
Quantum Machine Learning investigates the pos-sibility of quantum computers enhancing Machine Learning algorithms. Anomaly segmentation is a fundamental task in various domains to identify irregularities at sample level and can be addressed with both supervised and unsupervised methods. Autoencoders are commonly used in unsupervised tasks, where models are trained to reconstruct normal instances efficiently, al-lowing anomaly identification through high reconstruction errors. While quantum autoencoders have been proposed in the literature, their application to anomaly segmentation tasks remains unexplored. In this paper, we introduce a patch-based quantum autoencoder (QPB-AE) for image anomaly segmentation, with a number of parameters scaling logarithmically with patch size. QPB-AE reconstructs the quantum state of the embedded input patches, computing an anomaly map directly from measurement through a SWAP test without reconstructing the input image. We evaluate its performance across multiple datasets and parameter configurations and compare it against a classical counterpart.