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Quantum Physics

arXiv:2212.12220 (quant-ph)
[Submitted on 23 Dec 2022 (v1) , last revised 17 Jun 2023 (this version, v2)]

Title: Quantum Computing for Molecular Biology

Title: 量子计算在分子生物学中的应用

Authors:Alberto Baiardi, Matthias Christandl, Markus Reiher
Abstract: 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 very 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: 分子生物学和生物化学通过分子结构及其相互作用来解释生命世界中的微观过程,而这些相互作用本质上是量子力学的。 尽管这些相互作用的理论基础已经非常牢固,但求解相关的量子力学方程的计算却非常困难。 然而,生物学中大部分分子功能可以通过经典力学来理解,在经典力学中,电子和原子核的相互作用已经被映射到有效的经典替代势能函数上,这些势能函数模拟了原子甚至更大实体之间的相互作用。 这些势能的简单数学结构提供了巨大的计算优势;然而,这种优势是以忽略所有量子关联以及相互作用的严格多粒子性质为代价的。 在本工作中,我们讨论量子计算如何通过为生物分子的模拟提供计算优势,从而提高分子生物学量子基础的实际应用价值。 我们不仅在此背景下讨论生物分子电子结构的典型量子力学问题,还考虑占主导地位的经典问题(如蛋白质折叠和药物设计)以及生物信息学的数据驱动方法,并探讨它们在多大程度上可能适用于量子模拟和量子计算。
Comments: 76 pages, 7 figures
Subjects: Quantum Physics (quant-ph) ; Strongly Correlated Electrons (cond-mat.str-el); Biological Physics (physics.bio-ph); Chemical Physics (physics.chem-ph); Computational Physics (physics.comp-ph)
Cite as: arXiv:2212.12220 [quant-ph]
  (or arXiv:2212.12220v2 [quant-ph] for this version)
  https://doi.org/10.48550/arXiv.2212.12220
arXiv-issued DOI via DataCite
Journal reference: ChemBioChem 24 (2023) e202300120
Related DOI: https://doi.org/10.1002/cbic.202300120
DOI(s) linking to related resources

Submission history

From: Markus Reiher [view email]
[v1] Fri, 23 Dec 2022 09:23:04 UTC (8,272 KB)
[v2] Sat, 17 Jun 2023 15:30:49 UTC (6,499 KB)
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