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用于科学计算的量子算法。

Quantum algorithms for scientific computing.

作者信息

Au-Yeung R, Camino B, Rathore O, Kendon V

机构信息

Department of Physics, University of Strathclyde, Glasgow G4 0NG, United Kingdom.

Department of Chemistry, UCL, London WC1E 6BT, United Kingdom.

出版信息

Rep Prog Phys. 2024 Oct 29;87(11). doi: 10.1088/1361-6633/ad85f0.

Abstract

Quantum computing promises to provide the next step up in computational power for diverse application areas. In this review, we examine the science behind the quantum hype, and the breakthroughs required to achieve true quantum advantage in real world applications. Areas that are likely to have the greatest impact on high performance computing (HPC) include simulation of quantum systems, optimization, and machine learning. We draw our examples from electronic structure calculations and computational fluid dynamics which account for a large fraction of current scientific and engineering use of HPC. Potential challenges include encoding and decoding classical data for quantum devices, and mismatched clock speeds between classical and quantum processors. Even a modest quantum enhancement to current classical techniques would have far-reaching impacts in areas such as weather forecasting, aerospace engineering, and the design of 'green' materials for sustainable development. This requires significant effort from the computational science, engineering and quantum computing communities working together.

摘要

量子计算有望为不同应用领域带来计算能力的进一步提升。在本综述中,我们审视了量子热潮背后的科学原理,以及在实际应用中实现真正量子优势所需的突破。对高性能计算(HPC)可能产生最大影响的领域包括量子系统模拟、优化和机器学习。我们从电子结构计算和计算流体动力学中选取示例,这些领域在当前HPC的科学和工程应用中占了很大比例。潜在挑战包括为量子设备编码和解码经典数据,以及经典处理器和量子处理器之间不匹配的时钟速度。即使对当前经典技术进行适度的量子增强,也将在天气预报、航空航天工程以及可持续发展的“绿色”材料设计等领域产生深远影响。这需要计算科学、工程和量子计算领域的社区共同付出巨大努力。

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