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arXiv 2004.00704quant-ph
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使用连续时间演化的量子计算

Quantum computing using continuous-time evolution

Viv Kendon

摘要:

计算方法是我们除了科学实验之外,探索复杂生物系统特性的最有效工具。进展正在放缓,因为数字硅计算机在速度方面已经达到了极限。其他类型的计算使用完全不同的架构,包括神经形态和量子,承诺在速度和效率方面取得突破。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。这为解决某些类型的计算问题提供了一条从根本上更有效的途径,包括与生物模拟相关的几个问题。特别是,优化问题,凸和非凸,功能在许多生物模型,包括蛋白质折叠和分子动力学。早期的量子计算机将很小,让人想起早期的数字硅计算。了解如何利用第一代量子硬件对于在生物模拟和下一代量子计算机的开发方面取得进展至关重要。这篇综述概述了量子计算的当前状态和未来前景,并提供了一些指示如何以及在哪里应用它来加速生物模拟中的瓶颈。

英文摘要:

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, optimisation 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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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