How quantum computing can enhance biomarker discovery

TL;DR

This paper explores how quantum computing, especially quantum machine learning algorithms, can improve biomarker discovery by handling complex, multi-modal healthcare data more effectively, addressing current challenges and future opportunities.

Contribution

It maps quantum algorithms to biomarker discovery applications across various data types and discusses associated opportunities and challenges.

Findings

Quantum algorithms can process complex healthcare data more efficiently.

Potential to identify early biomarkers for multi-factorial diseases.

Highlights open research challenges in quantum-enhanced biomarker discovery.

Abstract

Biomarkers play a central role in medicine's gradual progress towards proactive, personalized precision diagnostics and interventions. However, finding biomarkers that provide very early indicators of a change in health status, for example for multi-factorial diseases, has been challenging. Discovery of such biomarkers stands to benefit significantly from advanced information processing and means to detect complex correlations, which quantum computing offers. In this perspective paper, quantum algorithms, particularly in machine learning, are mapped to key applications in biomarker discovery. The opportunities and challenges associated with the algorithms and applications are discussed. The analysis is structured according to different data types - multi-dimensional, time series, and erroneous data - and covers key data modalities in healthcare - electronic health records (EHRs), omics, and medical images. An outlook is provided concerning open research challenges.