Application of Quantum Computing to Biochemical Systems: A Look to the Future
Open accessAbstract
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 effective strategies split the work over classical and quantum computers. There is a proven set of methods in computational chemistry and materials physics that has used this same idea of splitting a complex physical system into parts that are treated at different levels of theory to obtain solutions for the complete physical system for which a brute force solution with a single method is not feasible. These methods are variously known as embedding, multi-scale, and fragment techniques and methods. We review these methods and then propose the embedding approach as a method for describing complex biochemical systems, with the parts not only treated with different levels of theory, but computed with hybrid classical and quantum algorithms. Such strategies are critical if one wants to expand the focus to biochemical molecules that contain active regions that cannot be properly explained with traditional algorithms on classical computers. While we do not solve this problem here, we provide an overview of where the field is going to enable such problems to be tackled in the future.
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Quantum computing can contribute to biochemical systems, biology, biochemistry, computation of protein-ligand interplay, mRNA codon optimization, and protein folding with higher-order speeds compared with conventional computers.
“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.”
From Quantum Computing in the Next-Generation Computational Biology Landscape: From Protein Folding to Molecular Dynamics (2024) · cites this paper as [7]
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Prajapati, M., & Killana, K. B. (2024) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-2147-4_28 · Cited by 3 · see citing works
Quantum Biochemistry: Unveiling the Quantum Coherence in Enzyme Catalysis
Abbas, A., Abid, M.-U.-H., Mahnoor, H., Bukhari, M., Malik, R., Haider, M. J., Rafique, R., Fatima, M., & Shoaib, T. (2023) · FRONTIERS IN CHEMICAL SCIENCES · doi:10.52700/fcs.v4i2.75 · Cited by 2 · see citing works
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Population Health Management and the Science of Individuality in the Era of Quantum Computing
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Artificial Intelligence in Biomaterials: Revolutionizing Design and Manufacturing
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Perspective on the Role of Quantum Mechanical Calculations on Cellular Molecular Interactions
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The Outlook of German Companies on IT-Security and Their Readiness for Quantum Computing
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Modelling Heterogeneous Catalysis using Quantum Computers: An academic and industry perspectivePreprint
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Algebraic decoding for classes of quantum codes
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Mental Health Prediction Using Quantum Machine Learning Classifiers: A Comprehensive Analysis
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Frontier Technologies: AI, Synthetic Biology, and Omics Convergence
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- Authors
- Cheng, H.-P., Deumens, E., Freericks, J. K., Li, C., & Sanders, B. A.
- Affiliations
- University of Florida (US)
- Georgetown University (US)
- Corresponding author
- Freericks, James K.
- Venue
- Frontiers in Chemistry
- Publisher
- Frontiers Media SA
- Published
- 2020-11-24
- Volume
- 8
- Pages
- 587143-587143
- ISSN
- 2296-2646 (ISSN-L)
- PMC
- PMC7732423
- OpenAlex
- W3080756416
- Cited by
- 53
- References
- 62
- Citations by year
- 2026: 8 · 2025: 4 · 2024: 13 · 2023: 13 · 2022: 11 · 2021: 3
- FWCI
- 0.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Chemistry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- National Science Foundation
- U.S. Department of Energy
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Quantum Computing Algorithms and Architecture
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Keywords
- quantum computing, biochemical systems, hybrid quantum-classical algorithms, embedding method, multiscale methods, quantum advantage, near-term quantum devices, computational chemistry
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