Federated quantum machine learning for drug discovery and healthcare

Abstract

Integrating quantum computing with traditional high-performance systems holds great promise for revolutionizing preclinical drug discovery and healthcare. This combination can improve data processing capabilities, accelerate research timelines, and optimize the analysis of complex biological data, ultimately leading to more efficient and effective drug development processes. Quantum advancements can help streamline the drug development process, ultimately lowering both the time and costs involved. This chapter provides an overview of the recent advancements in Quantum Machine Learning (QML) and Federated Learning (FL), highlighting their transformative potential in preclinical drug discovery and healthcare. As the demand for innovative solutions in drug development and patient care increases, integrating quantum computing capabilities offers a new paradigm for tackling complex biomedical challenges.Additionally, the chapter discusses the role of FL and Quantum Federated Learning (QFL) in facilitating collaborative research while preserving data privacy, allowing multiple pharmaceutical and healthcare sectors to contribute to model training without sharing sensitive patient information. In the pursuit of innovative solutions for quality assurance within the pharmaceutical sector, we investigate the implementation of quantum federated learning utilizing variational quantum circuits for pill classification. Our focus is on effectively distinguishing between defective and non-defective pills, particularly in the context of unbalanced data distributions. Additionally, we explore the potential of integrating genomic sequencing within quantum federated settings to enhance classification tasks in healthcare, aiming to leverage the strengths of quantum computing for improved accuracy and efficiency in critical quality assurance processes. In addition, we provide a review of existing frameworks for current federated learning research. This chapter highlights the synergy between quantum technologies and federated learning approaches, showcasing key studies and methodologies that advance preclinical research, particularly in clinical trial outcome prediction, trial matching, and site selection, ultimately enhancing healthcare outcomes. By providing a thorough analysis of the current research landscape, this chapter aims to illuminate the challenges and opportunities within this promising field, fostering a deeper understanding of how these innovative techniques can revolutionize drug discovery and healthcare practices.