This paper focuses on algebraic-logical modeling of molecular genetic informatics systems and inherited biostructures. The focus is primarily on the development of a quantum-logical framework for modeling genetically inherited ensembles of coordinated cyclic (biorhythmic) processes characteristic of living organisms. The author develops a quantum-logical modeling approach based on the genetic alphabet of 4 unitary Hadamard matrices and cyclic power groups based on it. The possibilities and prospects of the proposed approach for the development of quantum-logical biology, genomorphic artificial intelligence, and biotechnology are discussed.
This paper focuses on algebraic-logical modeling of molecular genetic informatics systems and inherited biostructures. The focus is primarily on the development of a quantum-logical framework for modeling genetically inherited ensembles of coordinated cyclic (biorhythmic) processes characteristic of living organisms. The author develops a quantum-logical modeling approach based on the genetic alphabet of 4 unitary Hadamard matrices and cyclic power groups based on it. The possibilities and prospects of the proposed approach for the development of quantum-logical biology, genomorphic artificial intelligence, and biotechnology are discussed.
Quantum computing holds immense potential for revolutionizing various fields, particularly in scientific research and data analysis. However, the development of practical quantum algorithms is a complex and challenging endeavor. This paper proposes a novel approach to quantum algorithm design, leveraging bioinformatics to predict algorithm performance and efficiency. We outline a method that utilizes genomic data and protein structure analysis to create a predictive model, aiming to streamline the algorithm design process and accelerate discovery. The core mechanism centers around analyzing genetic sequences and protein interactions to identify patterns and correlations that correlate with algorithm performance. This will allow for a more focused and efficient exploration of potential quantum algorithms. The paper presents a framework for integrating biological data with traditional quantum algorithm development, demonstrating its potential for enhancing the overall design process. The research explores the application of specific bioinformatics tools and techniques for generating predictive models. The focus is on providing a robust, data-driven approach to guide algorithm optimization and reduce reliance on purely theoretical calculations. This work contributes to the growing field of bio-inspired quantum computing, offering a new avenue for accelerating the advancement of quantum technologies.
A study published in the international journal Briefings in Bioinformatics proposes the first clear definition and comprehensive four-tier algorithmic classification system for Quantum Bioinformatics, an emerging interdisciplinary field merging quantum mechanics and bioinformatics. It precisely categorizes the field into four sub-domains: Q-B, QCt-B, QCg-B, and QCr-B, based on three core elements: the nature of computational operations, the type of platform (classical/quantum computer), and the type of data (classical/quantum biological data). It provides a theoretical milestone to shift the paradigm of life science research by overcoming the limitations of classical computing in complex problems like massive genomics, drug design, and protein folding using quantum technology, while promoting interdisciplinary collaboration. [Quantum Biology Society] Bioinformatics has led to rapid advancements in modern life sciences and medicine by utilizing computer algorithms and mathematical models to analyze vast amounts of biological data. Recently, Quantum Bioinformatics has emerged as a new interdisciplinary field that introduces the principles of quantum mechanics into the analysis of biological systems. It is gaining attention as a promising tool to overcome complex challenges in molecular biology, drug design, and protein folding, which have been difficult to solve with traditional classical computers. However, because the goals and concepts of quantum biology and quantum bioinformatics are similar, confusion has arisen among scientists, and there have been difficulties in identifying purely quantum effects and establishing academic boundaries. To address these limitations, a study titled "New classifications for quantum bioinformatics," recently published in the international journal Briefings in Bioinformatics, proposed a comprehensive taxonomy to clarify research directions and promote interdisciplinary collaboration. A joint research team led by Professor Majid Mokhtari from the Kish International Campus of the University of Tehran in Iran established this crucial theoretical framework. ■ 3 Classification Criteria for Quantum Bioinformatics To clearly classify quantum bioinformatics, this study established three core elements as criteria: The Nature of Computational Operations: Distinguishes whether the tool is for simple data mining or for designing and modeling computational tools using quantum concepts. Type of Platform: Specifies whether the physical hardware performing the computation is a classical computer or a quantum computer. Type of Data: Differentiates whether the subject of analysis is classical biological data, such as genomics or proteomics, or quantum biological data extracted using quantum physics tools. ■ A New 4-Tier Taxonomy (Q-B, QCt-B, QCg-B, QCr-B) Based on the above criteria, the research team precisely classified quantum bioinformatics into the following four sub-domains: Q-Bioinformatics (Q-B): A field dedicated to developing and modeling computational tools that exclusively utilize quantum biological data derived from biological sources. This can be performed on both classical and quantum computer platforms. QCt-Bioinformatics (QCt-B): An area focused on designing and developing algorithms and tools that apply the principles of quantum mechanics (such as superposition and entanglement) to analyze conventional classical biological data. QCg-Bioinformatics (QCg-B): A field that applies tools and algorithms to analyze and mine classical biological data, utilizing either classical or quantum computers. QCr-Bioinformatics (QCr-B): A specialized approach that uses classical bioinformatics tools or QCt-B tools for its analytical methodology, but runs exclusively on quantum computer hardware to leverage its overwhelming computational speed and storage capacity. This clear classification system demonstrates that even researchers without direct access to physical quantum computer hardware can significantly contribute to quantum-based algorithm research (such as QCt-B) by utilizing classical computers. This study is evaluated as having eliminated academic confusion that may arise in the convergence of quantum computing and life sciences, laying a solid theoretical foundation that will revolutionize the paradigm of genomic analysis and molecular target discovery in the future. #QuantumBioinformatics #Bioinformatics #QuantumComputing #QuantumCognition #QuantumCryptography #Taxonomy #InterdisciplinaryResearch #QuantumBiology #Biophysics #KoreanQuantumBiologySociety https://pmc.ncbi.nlm.nih.gov/articles/PMC10939336/
The physical mechanism of heredity or inheritance of genes is a quantum mechanical and/or quantum computational process. A theory of bio-quantum genetics is established in this paper. Principle of Bio-quantum Genetics is suggested. I propose and define the soft-genes of genetics controlling the processes of heredity or inheritance of genes. This research deals with the quantum mechanisms of Mendel plant heredity and family inheritance as examples of bio-quantum genetics, deepening our understanding of heredity or inheritance. I believe that more contributions will be made to promote researches of bio-quantum genetics or quantum biology at large.
Molecular biology is the field of science concerned with studying the chemical structures and processes of biological phenomena that involve the basic units of life and molecules. The field of molecular biology is focused especially on nucleic acids and proteins, these molecules interact and behave within cells. Molecular biology emerged in the 1930s. When quantum computing combines with this DNA computing to form a new computing process, it is quantum molecular biology. This chapter discusses another form of quantum molecular biology with multi-valued logic. It describes multi-valued arithmetic circuits in DNA-quantum circuits. The system was divided into four parts. The first part contained the DNA system, then a DNA cache memory where the DNA sequences were stored, then the data conversion unit to convert the DNA information into qubits, and the last part was the quantum system.
The recent biotechnological progress has allowed life scientists and physicians to access an unprecedented, massive amount of data at all levels (molecular, supramolecular, cellular and so on) of biological complexity. So far, mostly classical computational efforts have been dedicated to the simulation, prediction or de novo design of biomolecules, in order to improve the understanding of their function or to develop novel therapeutics. At a higher level of complexity, the progress of omics disciplines (genomics, transcriptomics, proteomics and metabolomics) has prompted researchers to develop informatics means to describe and annotate new biomolecules identified with a resolution down to the single cell, but also with a high-throughput speed. Machine learning approaches have been implemented to both the modelling studies and the handling of biomedical data. Quantum computing (QC) approaches hold the promise to resolve, speed up or refine the analysis of a wide range of these computational problems. Here, we review and comment on recently developed QC algorithms for biocomputing, with a particular focus on multi-scale modelling and genomic analyses. Indeed, differently from other computational approaches such as protein structure prediction, these problems have been shown to be adequately mapped onto quantum architectures, the main limit for their immediate use being the number of qubits and decoherence effects in the available quantum machines. Possible advantages over the classical counterparts are highlighted, along with a description of some hybrid classical/quantum approaches, which could be the closest to be realistically applied in biocomputation.