Quantum Computing and Machine Learning for Transforming Precision Medicine and Drug Discovery

Author:

Yenduri Lakshmana Kumar1ORCID,M. H. Chaithra2,Shahedhadeennisa S.3ORCID,Annamalai M.4ORCID,Rajesh L.5ORCID,Imran Shaik Mohammed6ORCID

Affiliation:

1. Apple Pay Generative AI Applications, Visa Inc, USA

2. School of Computer Science and Engineering, REVA University, Bengaluru, India

3. Computer Science and Engineering, Dayananda Sagar University, Bengaluru, India

4. Business Solution and Services, Vinsys IT Services India Limited, Pune, India

5. Information Science and Engineering, Dayananda Sagar University, Bengaluru, India

6. Computer Science and Engineering (AI & ML), Dhulapally Malla Reddy Engineering College for Women, India

Abstract

Quantum Machine Learning can be considered one of the transformational technologies in precision medicine and drug discovery because quantum computing combines immense processing power with high capability, predictive machine learning. This chapter will discuss the potential for QML to revolutionize complex biological data analysis to rapidly identify disease biomarkers and highly personalized treatment approaches. The chapter describes the two superior performances that can be achieved by quantum algorithms in simulating molecular interactions, and thereby drastically reducing time and cost in drug development. It also discusses key applications that include quantum-enhanced neural networks and support vector machines to diagnose diseases and predict outcomes of treatments. Challenges regarding scalability, noise reduction, and hardware limitations are discussed together with some very promising future directions. When quantum technology has reached full maturity, the opportunities it will give to machine learning will provide unparalleled breakthroughs in medical research.

Publisher

IGI Global

Reference19 articles.

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