量子生物学的奇异值分解量子算法

IF 3.7 Q2 CHEMISTRY, PHYSICAL ACS Physical Chemistry Au Pub Date : 2024-05-17 DOI:10.1021/acsphyschemau.4c00018
Emily K. Oh, Timothy J. Krogmeier, Anthony W. Schlimgen, Kade Head-Marsden
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引用次数: 0

摘要

最近,人们对利用当前量子计算机对非单元量子动力学进行建模和预测的量子算法产生了浓厚的兴趣。量子生物学领域是这些算法可能证明有用的一个领域,因为生物系统通常难以以完整形式处理,但适合采用开放的量子系统方法。在这里,我们介绍了最近开发的奇异值分解(SVD)算法在量子生物学两个系统中的应用:通过芬纳-马修斯-奥尔森复合体的激子能量传输和鸟类导航的激子对机制。我们通过在量子模拟器上的实施,证明了 SVD 算法能够准确捕捉这些系统的短期和长期动态,并得出结论:虽然该算法的实施超出了当前量子计算机的能力范围,但它有可能成为未来研究量子生物学相关系统的有效工具。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Singular Value Decomposition Quantum Algorithm for Quantum Biology
There has been a recent interest in quantum algorithms for the modeling and prediction of nonunitary quantum dynamics using current quantum computers. The field of quantum biology is one area where these algorithms could prove to be useful as biological systems are generally intractable to treat in their complete form but amenable to an open quantum systems approach. Here, we present the application of a recently developed singular value decomposition (SVD) algorithm to two systems in quantum biology: excitonic energy transport through the Fenna–Matthews–Olson complex and the radical pair mechanism for avian navigation. We demonstrate that the SVD algorithm is capable of capturing accurate short- and long-time dynamics for these systems through implementation on a quantum simulator and conclude that while the implementation of this algorithm is beyond the reach of current quantum computers, it has the potential to be an effective tool for the future study of systems relevant to quantum biology.
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来源期刊
CiteScore
3.70
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0.00%
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期刊介绍: ACS Physical Chemistry Au is an open access journal which publishes original fundamental and applied research on all aspects of physical chemistry. The journal publishes new and original experimental computational and theoretical research of interest to physical chemists biophysical chemists chemical physicists physicists material scientists and engineers. An essential criterion for acceptance is that the manuscript provides new physical insight or develops new tools and methods of general interest. Some major topical areas include:Molecules Clusters and Aerosols; Biophysics Biomaterials Liquids and Soft Matter; Energy Materials and Catalysis
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