Quantum Autoencoder Detects Brain MRI Anomalies with High Accuracy

A novel approach for anomaly detection in brain MRI data has been developed using an interpretable quantum autoencoder (QAE). This method leverages angle encoding to transform image patches into quantum states, then employs a variational encoder-decoder architecture trained to discard non-essential information via auxiliary "trash" qubits. The QAE identifies anomalies by measuring how resistant an input is to compression compared to normal data, with higher scores indicating deviations from the learned normal manifold. Evaluated on public brain MRI datasets, the QAE achieved impressive results, including a slice-level ROC-AUC of approximately 0.95 and a patch-level ROC-AUC of 0.813, surpassing classical autoencoder and PCA benchmarks. The system also generates spatially localized anomaly heatmaps that correlate with tumorous regions, offering a principled and controllable mechanism for medical imaging decision support.