New classifications for quantum bioinformatics: Q-bioinformatics, QCt-bioinformatics, QCg-bioinformatics, and QCr-bioinformatics

Abstract

Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.

Keywords: Quantum bioinformatics; Quantum biology; Quantum mechanics.

Figure 1

Diverse Classes in Quantum Bioinformatics. This figure illuminates multiple classes within the realm of Quantum Bioinformatics. It delineates Quantum Bioinformatics (Q-Bioinformatics / Q-B), Quantum Concept in Bioinformatics (QCt-Bioinformatics / QCt-B), Quantum Computing in Bioinformatics (QCg-Bioinformatics / QCg-B), and Quantum Computer in Bioinformatics (QCr-Bioinformatics / QCr-B). Each class is characterized by specific computational operations, platform types, and data categories, providing a comprehensive snapshot of the diverse applications and methodologies within the field.

Figure 2

Quantum Concepts in Biology. In Figure 2, quantum biology is organized in accordance with the principles of quantum mechanics that can produce biological consequences.

Figure 3

Exploring Quantum Biology and Bioinformatics. This figure provides a concise overview of key concepts in the realm of biological data, quantum biology, and quantum bioinformatics. It illustrates the shift from classical biological data to quantum biological data and highlights the objectives and applications of quantum biology and bioinformatics in leveraging quantum principles for biological research.