quantum-autoencoder-mri-anomaly

name	quantum-autoencoder-mri-anomaly
description	Quantum autoencoder (QAE) for compression-driven anomaly detection in brain MRI. Uses angle encoding, variational encoder-decoder with trash qubits, and incompressibility-based anomaly scoring. Achieves ROC-AUC ~0.95 slice-level and ~0.813 patch-level with spatially localized anomaly heatmaps. Use when: quantum anomaly detection, brain MRI analysis, quantum autoencoder design, compression-based medical diagnostics, trash qubit encoding, variational quantum encoders.
metadata	{"arxiv_id":"2606.27411","published":"2026-06-25","authors":"Santanu Ganguly, Xing Liang, Dimitrios Makris","tags":["quantum-autoencoder","brain-MRI","anomaly-detection","medical-imaging","compression","trash-qubits"]}

Quantum Autoencoder for Brain MRI Anomaly Detection

Core Concept

Anomaly detection in brain MRI using a quantum autoencoder trained to compress normal data. Anomalies (tumors, lesions) are identified by their resistance to compression relative to the learned normal manifold — higher compression resistance = higher anomaly score.

Architecture

Encoding

• Angle encoding: Maps image patches into quantum states via rotational angles
• Patch-based processing: Operates on image patches rather than full volumes

Variational Encoder-Decoder

• Encoder: Variational circuit compressing input into latent quantum state
• Trash qubits: Auxiliary qubits explicitly trained to discard information
• Decoder: Reconstructs from compressed latent representation

Anomaly Scoring

• Anomaly score = degree of input resistance to compression
• Normal data: High compression, low reconstruction error
• Anomalous data: Low compression (resists learned manifold), high reconstruction error

Key Findings

Metric	Value
Slice-level ROC-AUC	~0.95
Patch-level ROC-AUC	~0.813
Baselines outperformed	Classical autoencoder, PCA

Encoder-Decoder Asymmetry

• Effective anomaly detection arises from structured information compression within the encoder
• NOT from increased parameter magnitude or decoder expressivity
• Results in controlled compression-reconstruction trade-off with clear operating regime for principled threshold selection

Spatial Localization

• QAE produces spatially localized anomaly heatmaps aligned with tumorous regions
• Enables interpretable anomaly detection beyond binary classification

Implementation Pattern

# Conceptual architecture
class QuantumAnomalyDetector:
    def __init__(self, n_qubits, n_trash):
        self.encoder = VariationalQuantumCircuit(n_qubits)
        self.decoder = VariationalQuantumCircuit(n_qubits - n_trash)
        self.n_trash = n_trash  # auxiliary qubits for discarding info
    
    def anomaly_score(self, patch):
        """Higher score = more anomalous"""
        encoded = self.encode_angle(patch)
        compressed = self.encoder(encoded)
        trash_state = compressed[:self.n_trash]  # discarded information
        reconstructed = self.decoder(compressed[self.n_trash:])
        return reconstruction_error(patch, reconstructed)
    
    def encode_angle(self, patch):
        # Map pixel values to rotation angles
        angles = normalize(patch) * pi
        return prepare_rotated_state(angles)

Compression-Reconstruction Trade-off

The QAE operates in a distinct regime where:

• Compression: Encoder actively discards information to trash qubits
• Reconstruction: Decoder must work with compressed representation
• Anomaly detection: Inputs outside the learned normal manifold resist compression, yielding higher reconstruction errors

This provides a principled threshold selection mechanism based on the operating regime boundary.

Applications

• Brain tumor detection and localization
• Anomaly detection in medical imaging workflows
• Decision support for radiologists
• Studying compression dynamics in quantum machine learning

Pitfalls

• Patch-level vs slice-level performance: Patch-level ROC-AUC (~0.813) lower than slice-level (~0.95) — patch granularity introduces more false positives
• DICOM dataset specificity: Evaluated on publicly available brain MRI DICOM datasets — generalization to other modalities requires retraining
• Quantum hardware constraints: Current results from simulation; actual quantum hardware may show different performance due to noise
• Threshold calibration: Anomaly score thresholds must be calibrated per dataset — no universal threshold

Activation Keywords

• quantum autoencoder, brain MRI anomaly detection, compression-driven diagnosis, trash qubit encoding, variational quantum encoder-decoder, quantum medical imaging, anomaly heatmap, incompressibility-based detection, angle encoding quantum, DICOM quantum analysis