Molecular biology and biochemistry interpret microscopic processes in the living world in terms of molecular structures and their interactions, which are quantum mechanical by their very nature. Whereas the theoretical foundations of these interactions are well established, the computational solution of the relevant quantum mechanical equations is very hard. However, much of molecular function in biology can be understood in terms of classical mechanics, where the interactions of electrons and nuclei have been mapped onto effective classical surrogate potentials that model the interaction of atoms or even larger entities. The simple mathematical structure of these potentials offers huge computational advantages; however, this comes at the cost that all quantum correlations and the rigorous many-particle nature of the interactions are omitted. In this work, we discuss how quantum computation may advance the practical usefulness of the quantum foundations of molecular biology by offering computational advantages for simulations of biomolecules. We not only discuss typical quantum mechanical problems of the electronic structure of biomolecules in this context, but also consider the dominating classical problems (such as protein folding and drug design) as well as data-driven approaches of bioinformatics and the degree to which they might become amenable to quantum simulation and quantum computation.
Molecular biology and biochemistry interpret microscopic processes in the living world in terms of molecular structures and their interactions, which are quantum mechanical by their very nature. Whereas the theoretical foundations of these interactions are well established, the computational solution of the relevant quantum mechanical equations is very hard. However, much of molecular function in biology can be understood in terms of classical mechanics, where the interactions of electrons and nuclei have been mapped onto effective classical surrogate potentials that model the interaction of atoms or even larger entities. The simple mathematical structure of these potentials offers huge computational advantages; however, this comes at the cost that all quantum correlations and the rigorous many-particle nature of the interactions are omitted. In this work, we discuss how quantum computation may advance the practical usefulness of the quantum foundations of molecular biology by offering computational advantages for simulations of biomolecules. We not only discuss typical quantum mechanical problems of the electronic structure of biomolecules in this context, but also consider the dominating classical problems (such as protein folding and drug design) as well as data-driven approaches of bioinformatics and the degree to which they might become amenable to quantum simulation and quantum computation.
Abstract Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to “hype,” and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics. This article is categorized under: Structure and Mechanism > Computational Biochemistry and Biophysics Data Science > Computer Algorithms and Programming Electronic Structure Theory > Ab Initio Electronic Structure Methods
Quantum computational biology is an advanced interdisciplinary field that integrates the principles of quantum mechanics with computational biology to solve complex biological problems, and for biomedical students and professionals, authoring a book on this subject would provide a foundational understanding of how quantum algorithms can model molecular interactions, simulate protein folding, analyze genomic data at unprecedented speeds, and optimize drug discovery processes, thereby bridging the gap between theoretical quantum computing and practical biomedical applications to revolutionize diagnostics and therapeutics.
This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed within biological systems. This research proposes a framework that leverages these quantum phenomena, specifically focusing on quantum tunneling, superposition, and entanglement, to address complex challenges in biological data analysis. The core mechanism involves simulating quantum effects during processes like protein folding and DNA sequence recognition, and utilizing quantum algorithms – such as Grover's algorithm and quantum annealing – to optimize sequence data analysis. We present a conceptual approach to formulating biological problems as quantum computing problems, offering a fundamentally new perspective for bioinformatics. The potential impact of this approach lies in developing more efficient and accurate algorithms for tasks including genomic sequence alignment, protein structure prediction, and drug discovery. This work aims to bridge the gap between quantum mechanics and biology, paving the way for a new era of bioinformatics driven by quantum principles. ---
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.
This paper introduces "Quantum Biologist," a novel quantum simulator designed to accurately model complex interactions between biological molecules. The core premise centers on harnessing quantum entanglement to accelerate simulations and enhance precision, addressing the limitations of classical computing in capturing quantum effects within biological systems. The simulator leverages quantum bits (qubits) and quantum gates to represent the quantum states of biomolecules, such as enzymes and proteins, enabling parallel computation through entanglement. We detail the theoretical framework, outlining the key mathematical formulations and operational principles. The simulation architecture is described, focusing on the representation of molecular states and the implementation of relevant quantum algorithms. The potential impact of Quantum Biologist on accelerating drug design and advancing our understanding of biological processes is explored. We provide a preliminary analysis of the computational complexity and scalability of the simulator. ---
Quantum computing is emerging as a transformative approach in bioinformatics and quantum chemistry, addressing fundamental computational limitations of classical methods in molecular science. Many biological and chemical systems are inherently governed by quantum mechanics, making accurate simulation with classical algorithms computationally expensive or infeasible. This review examines how quantum computing, particularly in the Noisy Intermediate-Scale Quantum (NISQ) era, is advancing molecular modeling, reaction dynamics, and bioinformatics applications. Key quantum algorithms, including the Variational Quantum Eigensolver (VQE) and Quantum Phase Estimation (QPE), demonstrate increasing capability in approximating molecular energies and simulating quantum systems. In parallel, applications in bioinformatics—such as protein folding, genomic search, and combinatorial optimization—highlight the potential of quantum computing to address complex biological problems. Quantum machine learning further extends these capabilities by integrating quantum algorithms with data-driven modeling. However, current progress is constrained by NISQ-era limitations, including noise, limited qubit counts, and scalability challenges. These constraints highlight the need for hybrid quantum–classical approaches and improved error mitigation strategies. Overall, quantum computing represents a complementary computational paradigm that can enhance classical methods in bioinformatics and chemistry. Continued advances in quantum algorithms, hardware, and hybrid modeling frameworks are expected to expand its role in molecular science and computational biology.