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Quantum computing using continuous-time evolution
Interface Focus ( IF 4.4 ) Pub Date : 2020-10-16 , DOI: 10.1098/rsfs.2019.0143
Viv Kendon 1
Affiliation  

Computational methods are the most effective tools we have besides scientific experiments to explore the properties of complex biological systems. Progress is slowing because digital silicon computers have reached their limits in terms of speed. Other types of computation using radically different architectures, including neuromorphic and quantum, promise breakthroughs in both speed and efficiency. Quantum computing exploits the coherence and superposition properties of quantum systems to explore many possible computational paths in parallel. This provides a fundamentally more efficient route to solving some types of computational problems, including several of relevance to biological simulations. In particular, optimization problems, both convex and non-convex, feature in many biological models, including protein folding and molecular dynamics. Early quantum computers will be small, reminiscent of the early days of digital silicon computing. Understanding how to exploit the first generation of quantum hardware is crucial for making progress in both biological simulation and the development of the next generations of quantum computers. This review outlines the current state-of-the-art and future prospects for quantum computing, and provides some indications of how and where to apply it to speed up bottlenecks in biological simulation.



中文翻译:

使用连续时间演化的量子计算

除了科学实验之外,计算方法是我们探索复杂生物系统特性的最有效工具。由于数字硅计算机在速度方面已达到极限,因此进度正在放缓。使用完全不同的架构的其他类型的计算(包括神经形态和量子)有望在速度和效率上取得突破。量子计算利用量子系统的相干性和叠加性来并行探索许多可能的计算路径。这提供了从根本上更有效的途径来解决某些类型的计算问题,包括与生物学模拟相关的几种问题。特别是,在许多生物学模型中都存在凸和非凸的优化问题,包括蛋白质折叠和分子动力学。早期的量子计算机将变得很小,让人想起数字硅计算的早期。了解如何利用第一代量子硬件对于在生物仿真和下一代量子计算机的开发中取得进展至关重要。这篇综述概述了量子计算的最新技术和未来前景,并提供了一些迹象表明如何以及在何处应用量子计算来加快生物模拟的瓶颈。

更新日期:2020-10-16
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