Understanding the possible quantum-driven behaviors of biological systems could aid in treating injuries or in developing cures for diseases, but research in the field has been pushed to the sidelines. It’s time for that to change.
The initiation of the field ‘quantum biology’ didn’t take too long following the establishment of quantum mechanics during the late 19th century to the early 20th century. In the late 1920s, Bohr delivered a lecture on the possibility of using quantum mechanics in solving the mysteries of life. However, since the 1940s, the field has started getting considerable attention following the publication of Schrödinger’s book on defining life using physics principles. During the last hundred years of research in quantum biology, we have accomplished considerable progress in understanding crucial issues around key biology problems. In this chapter, I shall focus on summarizing most of the areas of biology where the quantum mechanical interventions have helped us understand underlying mechanisms. We could develop some applied technologies involving quantum principles that might be utilized to understand biological diseases and intervene in the associated disorders with appropriate means. A pinpointed historical progression in this novel field of research will mostly be presented. Thus, the information here will help readers and interested researchers understand the field properly and plan for new projects aiming at creating future breakthroughs.
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.
The continuous manifestations of emerging and reemerging infectious diseases across the world is championed by microorganisms particularly, viruses and bacteria. These manifestations consistently keep researchers at work in establishing approaches towards minimising the burden of infectious diseases both locally and internationally. Microorganisms, especially bacteria and viruses, possess very disparate features like the presence of sensors, in-built biological engines, immaculate communication means, processing architecture, among others. Researchers are introducing these biological entities into the quantum theory network where their dynamical physical properties and concepts can be used to develop response mechanisms during infection stages. This article aims to review and present an account of quantum approaches derived from quantum mechanics as an emerging fundamental enabler and a novel concept in the treatment of infectious diseases. It also looks at the possible application of these quantum approaches in revolutionising discovery, development, and production of vaccines at a quantum scale and speed. A descriptive review of the literature was conducted by searching these electronic databases: Web of Science, PubMed, and SciELO. Findings in this article show that through the advent of single-particle approaches and high-resolution techniques, viral dynamics can be scrutinised during and after their assembly towards studying their infection development pathway. Additionally, quantum information in biological entities has been found to provide hereditary information insights supporting genetic studies in relation to infectious diseases. The introduction of quantum-inspired genetic algorithms towards predicting the secondary structures of RNA depicts certain superiority over strategies currently in existence. Ongoing research is putting quantum-based studies in an ideal direction towards shaping the understanding and perhaps treatment of infectious diseases.