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.
Top1. Introduction
In today's era of precision medicine and advanced healthcare technologies, biomedical data analysis has emerged as a linchpin in revolutionizing patient care, medical research, and the development of cutting-edge treatments. From mapping the complex pathways of the human genome to the rapid analysis of medical imaging, the importance of efficiently processing and understanding biomedical data cannot be overstated.
Biomedical data is often characterized by its immense volume, variety, and complexity. Consider, for instance, the human genome. With over 3 billion base pairs, even a minuscule sample of genomic data can comprise a vast array of sequences that hold clues to understanding a myriad of diseases. Similarly, modern medical imaging techniques like Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans produce a staggering amount of data in just seconds. Processing, analyzing, and interpreting this data requires robust computational tools(D. Maheshwari, B and D. Sierra-Sosa, 2022).
Classical computing, for all its accomplishments, is reaching its limits in grappling with the sheer scale and intricacies of biomedical data. Traditional computational methods, while powerful, often struggle with real-time processing needs, especially when it comes to tasks like pattern recognition in high-resolution images or simultaneous analysis of thousands of genes. Furthermore, many algorithms used in bioinformatics, such as those for protein folding prediction or sequence alignment, exhibit exponential time complexity on classical machines, meaning that as the size of the input grows, the time taken for computation can increase exponentially(J. Biamonte, and S. Lloyd, 2017).
Enter quantum computing—a paradigm-shifting approach to computation that harnesses the fundamental principles of quantum mechanics. Unlike classical bits that exist in a state of 0 or 1, quantum bits, or qubits, can exist in a superposition of states. This allows quantum computers to perform multiple calculations simultaneously(S. Rani, and P. Bhambri, 2023). When coupled with phenomena like entanglement, where qubits become interconnected and the state of one instantly affects the state of another, quantum computers promise to bring about computational speed-ups that are beyond the reach of classical systems.
The integration of quantum principles into machine learning presents a promising frontier known as Quantum Machine Learning (QML). QML can potentially address the limitations faced by classical machine learning models, especially in terms of computational efficiency and handling large-scale datasets. For biomedical data analysis, this means faster genomic sequencing, more accurate predictive modeling, and real-time analysis of medical images(P. B. Upama et al., 2023).
Artificial intelligence (AI) has emerged as a pivotal technology in the last decade. Its applications have seen significant advancements across multiple healthcare domains, including biomedicine, biomedical research, biomedical data processing, drug development, and disease diagnosis(D. Solenov, and J. F. Scherrer, 2018). The surge in electronic health records has paved the way for AI to revolutionize the healthcare sector. The outcomes produced by AI in numerous medical areas have been nothing short of remarkable. Machine learning holds the promise of enhancing healthcare quality through heightened efficiency and dependability. While the progress and potential of AI in healthcare are evident, it's crucial to also address the ethical dilemmas and challenges posed by its use. Presently, healthcare entities are keenly exploring ways AI can bolster patient health, ultimately leading to cost reductions in healthcare services.