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(Left) overall avidin protein structure with biotin binding; (Right) detailed biotin interaction amino acid residues from avidin.
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Chemistry is considered as one of the more promising applications to science of near-term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage. Because of the limitations of near-term quantum computers, the most ef...
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Reference-guided DNA sequencing and alignment is an important process in computational molecular biology. The amount of DNA data grows very fast, and many new genomes are waiting to be sequenced while millions of private genomes need to be re-sequenced. Each human genome has 3.2 B base pairs, and each one could be stored with 2 bits of information,...
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... Samo zagadnienie użyteczności obliczeń kwantowych w biologii i biochemii jest coraz częściej podejmowane w literaturze, np. w [3][4][5]. W artykule staramy się przedstawić aktualny stan zaawansowania wraz z ogólnym przeglądem wybranych algorytmów kwantowych i klasyczno-kwantowych oraz metod symulacji kwantowych z wykorzystaniem dostępnych komputerów kwantowych NISQ, które udostępniane są dla naukowców w ramach infrastruktury Poznańskiego Centrum Superkomputerowo--Sieciowego afiliowanego przy Instytucie Chemii Bioorganicznej Polskiej Akademii Nauk (ICHB PAN). Dodatkowo, w artykule opisujemy kluczowe założenia teoretyczne oraz zasady niezbędne do przygotowania podstawowych symulacji chemicznych z wykorzystaniem komputerów kwantowych NISQ. ...
Computer simulations using ever-increasing computing power and machine learning techniques allow advanced molecular modelling, molecular dynamics simulations and studies of intermolecular interactions. However, due to the complexity of biological systems and chemical processes at the molecular level, their accurate representation using classical computer models and techniques has faced a number of significant limitations for many years. A new and promising direction for the development of computational science and its potential applications in biochemistry is quantum computing and its integration with classical high-performance supercomputing systems. This article responds to the growing interest in the use of available quantum computers in exemplary applications. In this paper, we aim to provide an overview of the basic notions involved in the development of quantum algorithms and simulations related to issues at the interface of quantum chemistry and biochemistry. In addition, the article introduces the basic principles of performing simulations using the state-of-the-art quantum computers in the era of Noisy Intermediate-Scale Quantum (NISQ). Experimental results of the classical-quantum algorithm Variational Quantum Eigensolver (VQE) for example molecules H2 and CH+ are also presented. Despite the many shortcomings of currently available quantum computers, the analysed VQE algorithm proved to be effective in approximating the ground state of molecules using a minimal functional basis.
... The growing complexity of modern technology challenges, particularly in fields such as chemistry, materials science, and finance, has surpassed the capabilities of conventional classical computers. Quantum computing is promising for addressing these challenges, which were previously considered nearly impossible to solve [1][2][3][4][5][6][7][8][9][10][11][12][13]. Its significance has become even more pronounced because quantum computers have the potential to revolutionize cryptography [14]. ...
... Notable examples of the power of quantum parallelism include Shor's algorithm [124] and Grover's search algorithm [125]. Specifically, quantum computers' capabilities in optimization [13], cryptography [14], and scientific research [20,21] promise transformative breakthroughs in areas such as security [126], finance [10][11][12], drug discovery [3][4][5][6], and material science [7,8]. ...
An elementary review on principles of qubits and their prospects for quantum computing is provided. Due to its rapid development, quantum computing has attracted considerable attention as a core technology for the next generation and has demonstrated its potential in simulations of exotic materials, molecular structures, and theoretical computer science. To achieve fully error-corrected quantum computers, building a logical qubit from multiple physical qubits is crucial. The number of physical qubits needed depends on their error rates, making error reduction in physical qubits vital. Numerous efforts to reduce errors are ongoing in both existing and emerging quantum systems. Here, the principle and development of qubits, as well as the current status of the field, are reviewed to provide information to researchers from various fields and give insights into this promising technology.
... Quantum computing is anticipated to enable accurate simulation of chemical systems beyond the capabilities of classical methods. Whether this aim will be achieved with so-called noisy intermediate-scale quantum (NISQ) processors is still to be seen [1][2][3][4]. While devices are improving rapidly, NISQ applications also require algorithmic tools to mitigate noise and reduce required qubit counts. ...
Owing to the computational complexity of electronic structure algorithms running on classical digital computers, the range of molecular systems amenable to simulation remains tightly circumscribed even after many decades of work. Many believe quantum computers will transcend such limitations although in the current era the size and noise of these devices militates against significant progress. Here we describe a chemically intuitive approach that permits a subdomain of a molecule's electronic structure to be calculated accurately on a quantum device, while the rest of the molecule is described at a lower level of accuracy using density functional theory running on a classical computer. We demonstrate that this approach produces improved results for molecules that cannot be simulated fully on current quantum computers but which can be resolved classically at a cheaper level of approximation. The algorithm is tunable, so that the size of the quantum simulation can be adjusted to run on available quantum resources. Therefore, as quantum devices become larger, this method will enable increasingly large subdomains to be studied accurately.
... Furthermore, the field is essential to the calculation of experimental observables, the study of reactive events, and the building of sophisticated force fields at the classical and quantum mechanical levels. 5 Research in quantum biochemistry is primarily focused on finding ways to balance computational efficiency and accuracy in order to provide techniques that can accurately characterize the energy landscape of proteins and make mechanistic study easier. With an emphasis on enzyme catalysis, quantum biochemistry offers a stateof-the-art method for examining the electronic structure of biological molecules. ...
Objective: This study investigates the influence of quantum coherence in enzyme catalysis, aiming to elucidate its role in biochemical processes. The primary objective is to unravel quantum effects within various enzymes (A-F) involved in crucial biochemical pathways. Methodology: Employing a multidisciplinary approach, advanced experimental techniques and computational methods were utilized. Quantum tunneling rates were measured through Reaction Progress Kinetic Analysis (RPKA) with High-Performance Liquid Chromatography (HPLC). Femtosecond-resolved spectroscopy captured quantum coherence times, while Two-Dimensional Infrared Spectroscopy (2D IR) probed vibrational coupling. Ultrafast Laser Spectroscopy provided insights into enzyme dynamics. Density Functional Theory (DFT) calculations and Ab Initio Simulations complemented experimental findings. Results: The results reveal distinct quantum signatures across all enzymes. Notably, Enzyme A demonstrates a quantum tunneling rate of 3.2 x 10^-2 s^-1. Quantum coherence times in Enzyme B showcase unprecedented femtosecond scales, while other enzymes exhibit diverse behaviors. DFT calculations for Enzyme E predict a 30% reduction in energy barriers. Ab Initio Simulations of Enzyme F unveil persistent entanglement states. Conclusion: The observed quantum phenomena suggest a profound interplay between quantum coherence and enzyme catalysis, emphasizing the enzyme-specific nature of quantum effects. The implications of energy barrier reduction and entanglement states provide insights into potential quantum-assisted catalytic mechanisms.
... However, substantial focus is also given to the use of novel computing paradigms. In this regard, quantum computational techniques can answer complicated biological problems which require fast and numerous computations [1][2][3][4][5][6][7]. Computation in the quantum regime guarantees rapid speed using the quantum processor. ...
The review article presents the recent progress in quantum computing and simulation within the field of biological sciences. The article is designed mainly into two portions: quantum computing and quantum simulation. In the first part, significant aspects of quantum computing was illustrated, such as quantum hardware, quantum RAM and big data, modern quantum processors, qubit, superposition effect in quantum computation, quantum interference, quantum entanglement, and quantum logic gates. Simultaneously, in the second part, vital features of the quantum simulation was illustrated, such as the quantum simulator, algorithms used in quantum simulations, and the use of quantum simulation in biological science. Finally, the review provides exceptional views to future researchers about different aspects of quantum simulation in biological science.
... Quantum computing (QC) has emerged as the future for solving many problems more efficiently. For example, QC is used to simulate complex biochemical systems [1], reduce the training time of machine learning models [2], and create encryption methods for preventing cybersecurity threats [3]. Unlike classical computing, where the information is encoded as bits and each bit is assigned either 0 or 1, QC encodes the information as a list of quantum bit (qubit). ...
Quantum computing systems depend on the principles of quantum mechanics to perform multiple challenging tasks more efficiently than their classical counterparts. In classical software engineering, the software life cycle is used to document and structure the processes of design, implementation, and maintenance of software applications. It helps stakeholders understand how to build an application. In this paper, we summarize a set of software analytics topics and techniques in the development life cycle that can be leveraged and integrated into quantum software application development. The results of this work can assist researchers and practitioners in better understanding the quantum-specific emerging development activities, challenges, and opportunities in the next generation of quantum software.
... Simulation of physical quantum systems is widely established as future high-value use case for quantum computers [1,2]. Insights gained into the physical properties of molecules and materials have clear value for a wide range of applications where understanding the quantum nature of system is critical [3,4]. ...
We present an approach to combine novel molecular features with experimental data within a data-driven pipeline. The method is applied to the challenge of predicting the reactivity of a series of sulfonyl fluoride molecular fragments used for drug discovery of targeted covalent inhibitors. We demonstrate utility in predicting reactivity using features extracted from a workflow which employs quantum embedding of the reactive warhead using density matrix embedding theory, followed by Hamiltonian simulation of the resulting fragment model from an initial reference state. These predictions are found to improve when studying both larger active spaces and longer evolution times. The calculated features form a `quantum fingerprint' which allows molecules to be clustered with regard to warhead properties. We identify that the quantum fingerprint is well suited to scalable calculation on future quantum computing hardware, and explore approaches to capture results on current quantum hardware using error mitigation and suppression techniques. We further discuss how this general framework may be applied to a wider range of challenges where the potential for future quantum utility exists.
... (Quoted in Dyakonov, 2019). Not surprisingly, quantum computation is now beginning to suggest new models and analogies for understanding inorganic and organic phenomena (Cheng et al., 2020). Philosophers and engineers alike are providing new definitions of "machine" and "mechanisms", whether they refer to the macro or microscopic realms (Militello & Moreno, 2018). ...
This article addresses some crucial assumptions that are rarely acknowledged when organisms and machines are compared. We begin by presenting a short historical reconstruction of the concept of "machine." We show that there has never been a unique and widely accepted definition of "machine" and that the extant definitions are based on specific technologies. Then we argue that, despite the concept's ambiguity, we can still defend a more robust, specific, and useful notion of machine analogy that accounts for successful strategies in connecting specific devices (or mechanisms) with particular living phenomena. For that purpose, we distinguish between what we call "generic identity" and proper "machine analogy." We suggest that "generic identity"-which, roughly stated, presumes that some sort of vague similarity might exist between organisms and machines-is a source of the confusion haunting many persistent disagreements and that, accordingly, it should be dismissed. Instead, we endorse a particular form of "machine analogy" where the relation between organic phenomena and mechanical devices is not generic but specific and grounded on the identification of shared "invariants." We propose that the machine analogy is a kind of analogy as proportion and we elucidate how this is used or might be used in scientific practices. We finally argue that while organisms are not machines in a generic sense, they might share many robust "invariants," which justify the scientists' use of machine analogies for grasping living phenomena.
... However, in quantum computing, the fundamental essence is to keep information at the quantum level of matter and apply quantum gate action to evaluate that information by programming quantum interference. Then, coming to the scope of what quantum computing can achieve in biological interactions, its contribution is possible in biochemical systems [7], biology [8], biochemistry [9], computation of protein-ligand interplay [10], mRNA codon optimization [11], and protein folding [12] to name a few with higherorder speeds compared with conventional computers. To understand this higher velocity magnitude, we must accept that quantum computers work differently than their traditional counterparts. ...
Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.
... It is then natural to consider using a quantum computer to address the case of a quantum system embedded in a multiscale environment. Cheng et al. [225] review these methods and propose the embedding approach as a method for describing complex biochemical systems, with the parts treated at different levels of theory and computed with hybrid classical and quantum algorithms. ...
... Having come this far, we understand however that many commonly held views on the power of digital quantum computing, especially in NISQ times, are problematic. Cheng et al. [225] illustrate this problem : "Chemistry is considered as one of the more promising applications to science of near term quantum computing. Recent work in transitioning classical algorithms to a quantum computer has led to great strides in improving quantum algorithms and illustrating their quantum advantage." ...
... The bottom line is that if one wants to treat biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers, then one should not expect any quantum advantage from NISQ quantum computers. That said, Cheng et al. [225] provide a useful overview of how multiscale modeling involving quantum computers is going to enable biomolecular problems to be tackled in the future. ...
The last five years have seen a dramatic evolution of platforms for quantum computing, taking the field from physics experiments to quantum hardware and software engineering. Nevertheless, despite this progress of quantum processors, the field is still in the noisy intermediate-scale quantum (NISQ) regime, seriously limiting the performance of software applications. Key issues involve how to achieve quantum advantage in useful applications for quantum optimization and materials science, connected to the concept of quantum supremacy first demonstrated by Google in 2019. In this article we will describe recent work to establish relevant benchmarks for quantum supremacy and quantum advantage, present recent work on applications of variational quantum algorithms for optimization and electronic structure determination, discuss how to achieve practical quantum advantage, and finally outline current work and ideas about how to scale up to competitive quantum systems.
