hcq-alzheimer-quantum-classification

name	hcq-alzheimer-quantum-classification
description	Hybrid Classical-Quantum pipeline for Alzheimer's classification — supervised β-VAE + quantum kernels + quantum feature maps
category	neuroscience
trigger_words	["HCQ","alzheimer","quantum kernel","β-VAE","quantum SVM","quantum classification"]
arxiv_id	2606.14194

Hybrid Classical-Quantum (HCQ) Alzheimer's Classification via Supervised β-VAE and Quantum Kernels

Summary

Two-stage HCQ pipeline for binary AD classification from 3D T1-weighted MRI. Supervised 3D β-VAE compresses volumes to 64D latent code, PLS selects 6 disease-aware components, encoded via ZZ feature map onto 6-qubit register, then quantum kernel SVM. Achieves 72.1% accuracy, 0.799 AUC.

Core Methodology

• Category: cs.CV
• Authors: Tia Tiwari, Vamshi Krishna Kancharla, Neelam Sinha
• arXiv: 2606.14194

Key Concepts

• alzheimer
• quantum kernel
• β-VAE
• quantum SVM
• medical imaging
• hybrid quantum-classical
• quantum feature map
• MRI classification

Activation Triggers

HCQ, alzheimer, quantum kernel, β-VAE, quantum SVM, quantum classification