QML, short for quantum machine learning, is experiencing rapid growth as it combines the power of quantum computers with machine learning methods to enhance pattern identification and prediction. QML has demonstrated significant promise in biomedical research, since it has been used for the analysis of massive data sets from diverse sources, such as proteomics, genomics, and medical imaging. This chapter gives a broad overview of QML’s present status in biomedical applications, including both the theoretical underpinnings of the language and real-world implementations. First, we go over the fundamental ideas of quantum computing and machine learning, such as quantum gates, quantum circuits, and traditional machine learning techniques. The main difficulties in applying QML to biomedical applications are then discussed, including handling noisy quantum hardware and creating effective quantum algorithms for large, complicated data sets. We also go over some of the most exciting directions for this field’s research, such as the use of QML for illness diagnostics, personalized medicine, and drug development. In conclusion, we offer an array of case examples that demonstrate QML’s potential for use in biomedical research. These case studies address a variety of subjects, such as identifying protein-ligand binding affinities and using medical picture analysis to diagnose cancer. All things considered, this chapter offers a thorough review of QML’s current status in biomedical research, stressing both its promise and the obstacles that must be removed in order to fully realize it.
QML, short for quantum machine learning, is experiencing rapid growth as it combines the power of quantum computers with machine learning methods to enhance pattern identification and prediction. QML has demonstrated significant promise in biomedical research, since it has been used for the analysis of massive data sets from diverse sources, such as proteomics, genomics, and medical imaging. This chapter gives a broad overview of QML’s present status in biomedical applications, including both the theoretical underpinnings of the language and real-world implementations. First, we go over the fundamental ideas of quantum computing and machine learning, such as quantum gates, quantum circuits, and traditional machine learning techniques. The main difficulties in applying QML to biomedical applications are then discussed, including handling noisy quantum hardware and creating effective quantum algorithms for large, complicated data sets. We also go over some of the most exciting directions for this field’s research, such as the use of QML for illness diagnostics, personalized medicine, and drug development. In conclusion, we offer an array of case examples that demonstrate QML’s potential for use in biomedical research. These case studies address a variety of subjects, such as identifying protein-ligand binding affinities and using medical picture analysis to diagnose cancer. All things considered, this chapter offers a thorough review of QML’s current status in biomedical research, stressing both its promise and the obstacles that must be removed in order to fully realize it.
Quantum machine learning (QML) in the field of disease detection and prediction use quantum computing techniques and algorithms to analyze and classify large datasets of medical information, by identifying subtle patterns and predict the occurrence or progression of diseases. It involves applying machine learning techniques to data from biological and medical research, such as-genomic and proteomic data, medical imaging, electronic health records, and clinical trial data, using quantum computing algorithms and architectures to perform these analyses more efficiently and accurately than classical computing methods. This approach has the potential to provide new insights into complex biological systems and facilitate the development of more effective treatments and personalized medicine. In this paper, a systematic review of the use of QML algorithms has been conducted, which focuses on the detection and prediction of diseases among patients. The current essence of the field along with the challenges and limitations of current works have also been discussed. After evaluating the implemented and proposed methods of data analysis, algorithm development, usefulness and efficiency of the system in various disease detection and prediction, a recommendation was made on the open research scopes in this field at the end of the paper.
Quantum Machine Learning (QML) is an emerging discipline at the intersection of quantum computing and artificial intelligence, providing new approaches to address the increasing computational and analytical challenges in healthcare. This chapter explores how quantum principles, such as superposition, entanglement, and interference, can enhance the efficiency and accuracy of machine learning when applied to complex biomedical data. It presents an overview of the fundamental concepts of QML, compares its capabilities with those of traditional machine learning systems, and examines important algorithmic frameworks, including Quantum Support Vector Machines, Quantum Neural Networks, and Variational Quantum Algorithms. Practical examples are explored across several healthcare domains—drug discovery, medical imaging, genomic research, patient monitoring, and hospital resource management—showing how quantum methods are beginning to improve data interpretation and clinical decision-making. The chapter also outlines current limitations related to hardware capacity, data preparation, interpretability, and regulatory standards, while emphasizing ethical and cybersecurity considerations that accompany quantum-enabled systems. Finally, it looks ahead to a future in which accessible quantum computing, open-source development, and collaboration among scientists, clinicians, and technologists drive a new generation of personalized, secure, and data-driven healthcare.
The emerging and novel field of utilizing quantum physics in the world of machine learning, with applications customized for biomedical data, is explored in detail in this chapter. The discussion begins with an introduction to quantum physics and machine learning, before going on to explain how the two fields can work together to revolutionize biomedical data processing. Quantum-enhanced algorithms and their ability to process massive and intricate biomedical datasets are discussed in depth. In applications such as protein folding prediction, genomic data processing, and real-time diagnostics, the inherent parallelism and superposition capabilities of quantum machine learning are on full display. Finally, the technical and ethical difficulties of combining quantum machine learning with biomedical data are assessed in depth. The authors also provide an outlook on the promising interdisciplinary subject of quantum-powered machine learning and its potential to radically alter the field of biomedical research and healthcare solutions.
Quantum technologies have become powerful tools for a wide range of application disciplines, which tend to range from chemistry to agriculture, natural language processing, and healthcare due to exponentially growing computational power and advancement in machine learning algorithms. Furthermore, the processing of classical data and machine learning algorithms in the quantum domain has given rise to an emerging field like quantum machine learning. Recently, quantum machine learning, become a quite challenging field in case of healthcare applications. As a result, quantum machine learning has become a common and effective technique for data processing and classification across a wide range of domains. Consequently, quantum machine learning is the most commonly used application of quantum computing. The main objective of this work is to present a brief overview of current state-of-the-art published articles between 2013 and 2021 to identify, analyze, and classify the different QML algorithms and applications in the biomedical field. Furthermore, the approach adheres to the requirements for conducting systematic literature review techniques such as research questions and quality metrics of the articles. Initially, we discovered 3149 articles, excluded the 2847 papers, and read the 121 full papers. Therefore, this research compiled 30 articles that comply with the quantum machine learning models, and quantum circuits are using biomedical data. Eventually, this article provides a broad overview of quantum machine learning limitations and future prospects.
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.
The growing volume of biomedical data from electronic health records, medical imaging, genomic sequencing, wearable Internet of Medical Things (IoMT) devices, and precision medicine has created new opportunities as well as significant computational challenges for healthcare. This chapter explores how Quantum Machine Learning (QML) can complement conventional machine learning by improving the analysis of complex, high-dimensional healthcare data through hybrid quantum–classical approaches. It introduces the fundamentals of quantum computing and discusses their application in genomics, medical imaging, drug discovery, IoMT-enabled healthcare, and cybersecurity. The chapter also presents an integrated framework that brings together quantum-enabled analytics, secure communication, and clinical decision support, while highlighting current challenges, ethical considerations, and future directions such as fault-tolerant quantum computing, explainable quantum AI, federated quantum learning, and post-quantum healthcare security.