federated-quantum-medical-diagnosis

name	federated-quantum-medical-diagnosis
description	Federated Quantum Neural Network methodology for privacy-preserving medical image diagnosis. Combines federated learning with quantum neural networks for distributed medical imaging analysis without sharing raw patient data. Use when: federated quantum learning, privacy-preserving medical AI, distributed quantum ML for healthcare, FQPDR pattern.
license	Complete terms in LICENSE.txt
metadata	{"arxiv_id":"2605.08324","published":"2026-05-08","tags":["quantum","federated-learning","medical-diagnosis","privacy","qnn"]}

Federated Quantum Medical Diagnosis

Overview

Combine federated learning (FL) with quantum neural networks (QNNs) for privacy-preserving medical image analysis. Each site trains a local QNN on private medical data, and only model parameters (not data) are aggregated.

When to Use

• Multi-institutional medical image analysis without data sharing
• Early detection of subtle medical features (microaneurysms, small lesions)
• Privacy-preserving quantum ML for healthcare
• Distributed quantum neural network training across hospitals

Architecture

Step 1: Local QNN Design

import pennylane as qml
import torch

def create_qnn(n_qubits, n_layers):
    dev = qml.device('default.qubit', wires=n_qubits)
    
    @qml.qnode(dev, interface='torch')
    def circuit(inputs, weights):
        # Data encoding (amplitude encoding for medical features)
        qml.AmplitudeEmbedding(inputs, wires=range(n_qubits), normalize=True)
        
        # Variational layers
        for layer in range(n_layers):
            for i in range(n_qubits):
                qml.Rot(weights[layer, i, 0], weights[layer, i, 1], 
                       weights[layer, i, 2], wires=i)
            for i in range(n_qubits - 1):
                qml.CNOT(wires=[i, i + 1])
        
        return qml.expval(qml.PauliZ(0))
    
    return circuit

Step 2: Federated Aggregation

def federated_aggregate(local_models, weights=None):
    """FedAvg for quantum neural networks"""
    if weights is None:
        weights = [1.0 / len(local_models)] * len(local_models)
    
    global_model = {}
    for key in local_models[0].keys():
        global_model[key] = sum(
            w * local_models[i][key] 
            for i, w in enumerate(weights)
        )
    return global_model

Step 3: Training Loop

for round in range(n_rounds):
    local_models = []
    for site in sites:
        # Train local QNN on private medical data
        model = train_local_qnn(site.data, site.labels)
        local_models.append(model.get_parameters())
    
    # Aggregate
    global_params = federated_aggregate(local_models)
    
    # Distribute global model back to sites
    for site in sites:
        site.set_parameters(global_params)

Key Patterns

Pattern 1: Quantum Feature Encoding for Medical Images

Use amplitude or angle encoding for medical image patches:

• Small image regions (patches) → quantum state preparation
• Pre-process with classical CNN for feature extraction
• Quantum circuit for fine-grained classification

Pattern 2: Privacy Preservation Guarantees

FL ensures raw patient data never leaves local site:

• Only model gradients/parameters are shared
• Optional: add differential privacy noise to gradients
• Quantum circuits add additional obfuscation

Pattern 3: Handling Class Imbalance

For rare disease detection (e.g., early DR):

• Use weighted loss functions in local training
• Oversample minority class with quantum data augmentation
• F1-score as primary metric (not accuracy)

Error Handling

Quantum Communication Overhead

QNNs require more classical communication than classical NNs:

• Compress quantum circuit parameters before transmission
• Use parameter-efficient ansatz (fewer variational parameters)
• Consider gradient compression techniques

Non-IID Data Across Sites

Medical data distribution varies by hospital:

• Use personalized FL (partial model sharing)
• Site-specific fine-tuning after global aggregation
• Monitor per-site performance separately

Related Skills

• quantum-medical-ai: General quantum medical AI patterns
• quantum-neural-network-designer: QNN architecture design
• medical-domain-adaptation: Medical image domain adaptation