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Quantum Bio-Informatics

PATH TO QUANTUM LIFE INFORMATION SCIENCES

Luigi Accardi, Masanori Ohya

Published Mar 1, 2008• 0 citations
Publisher

Abstract

As was explained in the Preface, the center QBIC was founded to seriously \nattempt a mutual interaction between bio-informatics and quantum information, \nthereby creating a new field that can be called quantum \nbio-informatics. The conference of the QBIC-2006 \nproceeding opens the 5 year program (that may be 8) for this quantum \nbio--informatics. This is an ambitious program whose goal is to put the \nmathematics and the physics of quantum mechanics, the recent developments of \nquantum information and quantum computer, the powerful tools of white noise \nanalysis and classical probability to the service of biology. \n \nWe will discuss here what we consider the characteristic of biological \nsystems and the path to the quantum bio-informatics.

DOI
10.1142/9789812793171_0002

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WORLD SCIENTIFIC eBooks•Jan 1, 2008•0 citations
As was explained in the Preface, the center QBIC was founded to seriously \nattempt a mutual interaction between bio-informatics and quantum information, \nthereby creating a new field that can be called quantum \nbio-informatics. The conference of the QBIC-2006 \nproceeding opens the 5 year program (that may be 8) for this quantum \nbio--informatics. This is an ambitious program whose goal is to put the \nmathematics and the physics of quantum mechanics, the recent developments of \nquantum information and quantum computer, the powerful tools of white noise \nanalysis and classical probability to the service of biology. \n \nWe will discuss here what we consider the characteristic of biological \nsystems and the path to the quantum bio-informatics.
This volume is based on the fifth international conference of quantum bio-informatics held at the QBI Center of Tokyo University of Science. This volume provides a platform to connect mathematics, physics, information and life sciences, and in particular, research for new paradigm for information science and life science on the basis of quantum theory. The following topics are discussed: Cryptographic algorithms Quantum algorithm and computation Quantum entanglement Quantum entropy and information dynamics Quantum dynamics and time operator Stochastic dynamics and white noise analysis Brain activity Quantum-like models and PD game Quantum physics and superconductivity Quantum tomography and sufficiency Adaptation in Plants Alignment of sequences Readership: Researchers in quantum information, quantum physics, bio-informatics and life sciences.
This paper introduces Quantum Bio-Inspired Computing (QBIC), a novel computational paradigm that leverages the inherent parallelism of biological neural networks to construct efficient quantum computing architectures. QBIC's core claim is that by simulating the complex, parallel processing capabilities of biological neural networks, we can build quantum computing systems capable of exceeding the limitations of traditional quantum algorithms. The central mechanism involves designing quantum neuron models based on quantum decoherence and entanglement, utilizing quantum circuits to emulate synaptic connections and inter-neuronal communication within biological neural networks, thereby enabling large-scale parallel computations. Unlike traditional approaches that directly implement quantum algorithms, QBIC represents a fundamentally new approach, drawing inspiration from biological computation and integrating it with quantum technologies. This offers the potential to tackle unstructured and complex datasets with unprecedented efficiency. The framework presented here provides a foundational approach for exploring the intersection of biology and quantum computing, potentially unlocking new computational frontiers. ---
This paper explores the potential of quantum algorithms to revolutionize bioinformatics, particularly in genomic analysis, protein structure prediction, and drug discovery. The core concept centers on leveraging the inherent information richness within biological datasets – specifically, DNA sequences and protein structures – to create novel quantum computational approaches. We propose a new algorithmic framework based on constructing quantum circuits directly from biological data structures, aiming to significantly accelerate computationally intensive tasks. The study emphasizes the transformative impact of this approach, focusing on the unique advantages conferred by biological information within the quantum realm. This research addresses the limitations of existing quantum computing approaches by integrating biological data into the core algorithm design.
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 Problems of Quantum-Like Representation in Economy Cognitive Science, and Genetics (L Accardi et al.) The Compound Fock Space and Its Application in Brain Models (K H Fichtner & W Freudenberg) On Quantum Algorithm for Multiple Alignment of Amino Acid Sequences (S Iriyama & M Ohya) Theory of Proton-Induced Superionic Conduction in Hydrogen-Bonded Systems (H Kamimurai) Quantum-Like Models for Decision Making in Psychology and Cognitive Science (A Khrennikov) On Completely Positive Non-Markovian Evolution of A d-Level System (A Kossakowski & R Rebolledo) Perspective from Micro-Macro Duality - Towards Non-Pertubative Renormalization Scheme (I Ojima) Some Aspects of Quadratic Generalized White Noise Functionals (Si Si & T Hida) Time in Physics and Life Science (I V Volovich) Note on Entropies in Quantum Processes (N Watanabe) Massive Collection of Full-Length Complementary DNA Clones and Microarray Analyses: Keys to Rice Transcriptome Analysis (S Kikuchi) A Basic Introduction to Gene Expression Studies Using Microarray Expression Data Analysis (D Wanke & J Kilian) and other papers.