quantum-enhanced-coronary-classification

name	quantum-enhanced-coronary-classification
description	Lightweight quantum-enhanced ResNet for coronary angiography (CAG) classification. Combines classical CNN backbones with variational quantum circuits for medical image classification. Use when: coronary angiography analysis, cardiac image classification, lightweight QML models, quantum-enhanced CNNs, operator-dependency reduction in CAG interpretation, or hybrid quantum-classical medical imaging.
metadata	{"arxiv_ids":"1809","published":"2026-01-22","tags":["quantum","medical-imaging","coronary","resnet","cnn","lightweight","vqc"]}

Quantum-Enhanced Coronary Angiography Classification

Description

Lightweight quantum-enhanced ResNet framework combining classical CNN feature extractors with variational quantum circuits for coronary angiography (CAG) classification. Addresses operator-dependency in CAG interpretation by providing consistent, quantum-enhanced analysis of coronary vessel images.

Key insight: Classical CNN handles most feature extraction; quantum circuit acts as a lightweight classifier head, leveraging Hilbert space expressivity for complex decision boundaries with minimal qubits.

Architecture

Hybrid Pipeline

Input Image → CNN Backbone (ResNet) → Feature Vector → Quantum Circuit → Classification

1. CNN Backbone: Standard ResNet (pretrained on ImageNet or medical data)
2. Feature Compression: Reduce to N dimensions (N = number of qubits)
3. Quantum Classifier Head: VQC with N qubits, parameterized layers
4. Measurement: Readout yields class probabilities

Quantum Circuit Design

• Encoding: Angle encoding of compressed features
• Ansatz: Hardware-efficient (RY/RZ + CZ entanglement layers)
• Layers: 2-4 layers (shallow to reduce noise impact)
• Readout: Pauli-Z expectation values per qubit

Why Lightweight?

• Only classifier head is quantum (1-4 qubits vs full image encoding)
• Classical CNN handles the computationally heavy feature extraction
• Quantum part focuses on high-dimensional decision boundaries
• Compatible with current NISQ hardware

When to Use

• Coronary angiography image classification
• Reducing operator dependency in CAG interpretation
• Lightweight quantum enhancement of existing CNN models
• Medical imaging where quantum advantage is plausible in classification layer
• Resource-constrained quantum hardware (few qubits available)

Implementation Steps

1. Prepare dataset: CAG images with stenosis/severity labels
2. Train/freeze CNN: Use pretrained ResNet, freeze early layers
3. Design VQC: Match qubit count to compressed feature dimension
4. Hybrid training: Backprop through CNN, parameter-shift for VQC
5. Evaluate: Compare against CNN-only baseline and clinical expert performance

Error Handling

Feature Dimension Mismatch

• Use PCA/autoencoder to compress CNN features to exact qubit count
• Ensure compressed features retain discriminative information

Quantum Circuit Barren Plateaus

• Initialize with identity or classically-informed parameters
• Use local observables instead of global measurements
• Limit circuit depth to avoid vanishing gradients

Clinical Validation Gap

• Validate against expert cardiologist annotations
• Report both accuracy and clinical relevance metrics (sensitivity, specificity)
• Consider regulatory requirements for clinical deployment

Resources

• Paper: Entity ID 1809 in kg.db
• Related: Hybrid quantum-classical patterns from quantum-medical-diagnosis skill
• Related: VQC design from quantum-neural-architecture skill