Optimal normalization in quantum-classical hybrid models for anti-cancer drug response prediction

TL;DR

This paper introduces a novel normalization strategy for quantum-classical hybrid models that enhances stability and prediction accuracy in anti-cancer drug response tasks, demonstrating improved performance over classical models.

Contribution

It proposes a moderated gradient-based normalization function to optimize data encoding in quantum-classical models, improving their stability and effectiveness.

Findings

QHML outperforms classical models with optimal normalization.

The normalization method stabilizes quantum-classical hybrid models.

Enhanced prediction accuracy in biomedical data analysis.

Abstract

Quantum-classical Hybrid Machine Learning (QHML) models are recognized for their robust performance and high generalization ability even for relatively small datasets. These qualities offer unique advantages for anti-cancer drug response prediction, where the number of available samples is typically small. However, such hybrid models appear to be very sensitive to the data encoding used at the interface of a neural network and a quantum circuit, with suboptimal choices leading to stability issues. To address this problem, we propose a novel strategy that uses a normalization function based on a moderated gradient version of the $\tanh$. This method transforms the outputs of the neural networks without concentrating them at the extreme value ranges. Our idea was evaluated on a dataset of gene expression and drug response measurements for various cancer cell lines, where we compared the prediction performance of a classical deep learning model and several QHML models. These results confirmed that QHML performed better than the classical models when data was optimally normalized. This study opens up new possibilities for biomedical data analysis using quantum computers.