Efficient quantum algorithm for lattice protein folding

IF 5.6 2区 物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY
Youle Wang and Xiangzhen Zhou
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引用次数: 0

Abstract

Predicting a protein’s three-dimensional structure from its primary amino acid sequence constitutes the protein folding problem, a pivotal challenge within computational biology. This task has been identified as a fitting domain for applying quantum annealing, an algorithmic technique posited to be faster than its classical counterparts. Nevertheless, the utility of quantum annealing is intrinsically contingent upon the spectral gap associated with the Hamiltonian of lattice proteins. This critical dependence introduces a limitation to the efficacy of these techniques, particularly in the context of simulating the intricate folding processes of proteins. In this paper, we address lattice protein folding as a polynomial unconstrained binary optimization problem, devising a hybrid quantum–classical algorithm to determine the minimum energy conformation effectively. Our method is distinguished by its logarithmic scaling with the spectral gap, conferring a significant edge over the conventional quantum annealing algorithms. The present findings indicate that the folding of lattice proteins can be achieved with a resource consumption that is polynomial in the lattice protein length, provided an ansatz state that encodes the target conformation is utilized. We also provide a simple and scalable method for preparing such states and further explore the adaptation of our method for extension to off-lattice protein models. This work paves a new avenue for surmounting complex computational biology problems via the utilization of quantum computers.
晶格蛋白折叠的高效量子算法
从蛋白质的初级氨基酸序列预测蛋白质的三维结构构成了蛋白质折叠问题,这是计算生物学中的一个关键挑战。这项任务已被确定为应用量子退火的拟合领域,量子退火是一种被认为比经典同行更快的算法技术。然而,量子退火的效用本质上取决于与晶格蛋白的哈密顿量相关的光谱间隙。这种关键的依赖性限制了这些技术的有效性,特别是在模拟蛋白质复杂折叠过程的背景下。本文将晶格蛋白折叠作为一个多项式无约束二元优化问题,设计了一种量子-经典混合算法来有效地确定最小能量构象。我们的方法的特点是它的对数尺度与谱间隙,赋予一个显著的优势优于传统的量子退火算法。目前的研究结果表明,如果利用编码目标构象的ansatz状态,晶格蛋白的折叠可以通过晶格蛋白长度的多项式资源消耗来实现。我们还提供了一种简单且可扩展的方法来制备这种状态,并进一步探索了我们的方法对扩展到离晶格蛋白质模型的适应性。这项工作为利用量子计算机解决复杂的计算生物学问题开辟了新的途径。
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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来源期刊
Quantum Science and Technology
Quantum Science and Technology Materials Science-Materials Science (miscellaneous)
CiteScore
11.20
自引率
3.00%
发文量
133
期刊介绍: Driven by advances in technology and experimental capability, the last decade has seen the emergence of quantum technology: a new praxis for controlling the quantum world. It is now possible to engineer complex, multi-component systems that merge the once distinct fields of quantum optics and condensed matter physics. Quantum Science and Technology is a new multidisciplinary, electronic-only journal, devoted to publishing research of the highest quality and impact covering theoretical and experimental advances in the fundamental science and application of all quantum-enabled technologies.
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