Quantum Variational Transformer Model for Enhanced Cancer Classification

TL;DR

This paper introduces a hybrid quantum-classical transformer model utilizing quantum attention mechanisms to improve cancer classification accuracy and efficiency, demonstrating significant performance gains over classical models on large genomic datasets.

Contribution

The study presents the first integration of quantum attention layers into transformer models for biomedical data, enhancing prediction accuracy and computational efficiency in cancer classification.

Findings

Achieved 92.8% accuracy and 0.96 AUC, outperforming classical models.

Demonstrated 35% faster training and 25% fewer parameters.

Validated the potential of quantum-enhanced transformers in biomedical applications.

Abstract

Accurate prediction of cancer type and primary tumor site is critical for effective diagnosis, personalized treatment, and improved outcomes. Traditional models struggle with the complexity of genomic and clinical data, but quantum computing offers enhanced computational capabilities. This study develops a hybrid quantum-classical transformer model, incorporating quantum attention mechanisms via variational quantum circuits (VQCs) to improve prediction accuracy. Using 30,000 anonymized cancer samples from the Genome Warehouse (GWH), data preprocessing included cleaning, encoding, and feature selection. Classical self-attention modules were replaced with quantum attention layers, with classical data encoded into quantum states via amplitude encoding. The model, trained using hybrid backpropagation and quantum gradient calculations, outperformed the classical transformer model, achieving 92.8% accuracy and an AUC of 0.96 compared to 87.5% accuracy and an AUC of 0.89. It also demonstrated 35% faster training and 25% fewer parameters, highlighting computational efficiency. These findings showcase the potential of quantum-enhanced transformers to advance biomedical data analysis, enabling more accurate diagnostics and personalized medicine.