An Amalgamation of Classical and Quantum Machine Learning For the Classification of Adenocarcinoma and Squamous Cell Carcinoma Patients

TL;DR

This paper presents a hybrid classical-quantum machine learning approach to accurately classify two subtypes of lung cancer using gene expression data, demonstrating the potential of quantum methods in biomedical classification tasks.

Contribution

It introduces a novel data representation method called QCrush and applies a Quantum Boltzmann Machine for cancer subtype classification, combining classical and quantum techniques.

Findings

Successful classification of lung cancer subtypes

Effective use of QCrush data representation

Demonstrated potential of quantum machine learning in biomedicine

Abstract

The ability to accurately classify disease subtypes is of vital importance, especially in oncology where this capability could have a life saving impact. Here we report a classification between two subtypes of non-small cell lung cancer, namely Adeno- carcinoma vs Squamous cell carcinoma. The data consists of approximately 20,000 gene expression values for each of 104 patients. The data was curated from [1] [2]. We used an amalgamation of classical and and quantum machine learning models to successfully classify these patients. We utilized feature selection methods based on univariate statistics in addition to XGBoost [3]. A novel and proprietary data representation method developed by one of the authors called QCrush was also used as it was designed to incorporate a maximal amount of information under the size constraints of the D-Wave quantum annealing computer. The machine learning was performed by a Quantum Boltzmann Machine. This paper will report our results, the various classical methods, and the quantum machine learning approach we utilized.