hcq-alzheimer-classification-vae-quantum-kernels

name	hcq-alzheimer-classification-vae-quantum-kernels
description	Hybrid Classical-Quantum pipeline for Alzheimer's classification using supervised β-VAE and quantum kernels (arXiv:2606.14194)
category	quantum-medical

Hybrid Classical-Quantum Alzheimer's Classification

Methodology from arXiv:2606.14194 (June 2026). Two-stage HCQ pipeline for binary AD classification from 3D structural MRI volumes.

Core Pattern

Classical and quantum components designed to complement rather than operate independently:

1. Supervised 3D β-VAE compresses MRI volumes into latent code using voxel-wise reconstruction + KL-divergence + focal classification losses
2. PLS regression selects disease-separating components and rescales into rotation angles
3. ZZ quantum feature map encodes onto qubit register
4. Precomputed-kernel SVM on quantum Gram matrix for classification

Key Findings

• 72.1% accuracy, 0.799 AUC on 308 ADNI-1 subjects (AD vs CN)
• Cross-fold variance halved with stability-enhanced variant
• Novelty: quantum kernel operates on disease-aware features learned end-to-end by supervised autoencoder
• 3D Grad-CAM validates model focus on Alzheimer's-linked brain regions

Implementation Steps

1. Resize 3D MRI volumes to standard dimensions (96×96×96)
2. Train supervised 3D β-VAE with reconstruction + KL + focal losses
3. Extract 64-dimensional latent codes
4. Apply PLS to select 6 components best separating classes
5. Encode components as rotation angles via ZZ quantum feature map
6. Compute N×N Gram matrix from quantum state overlaps
7. Train SVM on Gram matrix for classification
8. Validate with 3D Grad-CAM for biological plausibility

When to Use

• Binary classification from 3D medical imaging
• Scenarios where classical methods struggle with high-dimensional data
• Need for interpretable, biologically-grounded quantum ML
• Diagnostic classification across biomedical imaging domains

References

• arXiv: 2606.14194v1
• Authors: Tia Tiwari, Vamshi Krishna Kancharla, Neelam Sinha