Empirical Quantum Advantage Analysis of Quantum Kernel in Gene Expression Data

TL;DR

This paper investigates the potential quantum advantage in gene expression data classification by comparing quantum and classical machine learning methods, considering constraints like dataset suitability and feature relevance, with experimental validation on gene datasets.

Contribution

It provides an empirical analysis of quantum kernel methods on gene expression data, assessing quantum advantage, feature relevance, and computational complexity in practical scenarios.

Findings

Quantum methods show promising classification performance.

Feature relevance varies between classical and quantum approaches.

Quantum circuit complexity impacts real-world applicability.

Abstract

The incorporation of quantum ansatz with machine learning classification models demonstrates the ability to extract patterns from data for classification tasks. However, taking advantage of the enhanced computational power of quantum machine learning necessitates dealing with various constraints. In this paper, we focus on constraints like finding suitable datasets where quantum advantage is achievable and evaluating the relevance of features chosen by classical and quantum methods. Additionally, we compare quantum and classical approaches using benchmarks and estimate the computational complexity of quantum circuits to assess real-world usability. For our experimental validation, we selected the gene expression dataset, given the critical role of genetic variations in regulating physiological behavior and disease susceptibility. Through this study, we aim to contribute to the advancement of quantum machine learning methodologies, offering valuable insights into their potential for addressing complex classification challenges in various domains.