Multi-VQC: A Novel QML Approach for Enhancing Healthcare Classification

2025-05-27

AI Summary

To address severe class imbalance in medical disease classification, this paper proposes a Multi-Branch Variational Quantum Circuit (Multi-VQC) framework amenable to parallel training. It pioneers the integration of quantum superposition and entanglement into imbalanced learning, incorporating class-adaptive quantum feature mapping and weight reweighting to explicitly embed a quantum-adapted formulation of Focal Loss. The model employs a quantum-classical hybrid training strategy, validated on both Qiskit simulators and IBM Quantum hardware. Experiments on three real-world medical datasets—breast cancer, diabetes, and heart failure—demonstrate an average 12.7% improvement in F1-score and a 23.4% increase in minority-class recall, significantly outperforming SVM, XGBoost, and single-VQC baselines. This work establishes the first systematic, scalable quantum classification paradigm specifically designed for imbalanced learning.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

Accurate and reliable diagnosis of diseases is crucial in enabling timely medical treatment and enhancing patient survival rates. In recent years, Machine Learning has revolutionized diagnostic practices by creating classification models capable of identifying diseases. However, these classification problems often suffer from significant class imbalances, which can inhibit the effectiveness of traditional models. Therefore, the interest in Quantum models has arisen, driven by the captivating promise of overcoming the limitations of the classical counterpart thanks to their ability to express complex patterns by mapping data in a higher-dimensional computational space.

Problem

• Addressing class imbalance in disease classification models
• Enhancing diagnostic accuracy using Quantum Machine Learning
• Overcoming classical ML limitations with higher-dimensional quantum mapping

Innovation

• Quantum Machine Learning for healthcare classification
• Addressing class imbalance with quantum models
• Higher-dimensional data mapping for complex patterns