Development of a hybrid quantum-classical reservoir simulator and evaluation of quantum computing hardware

混合量子经典储层模拟器的开发和量子计算硬件的评估

• 批准号: 561106-2020
• 负责人: Chen, Zhangxing
• 金额: $ 3.64万
• 依托单位: University of Calgary
• 依托单位国家: 加拿大
• 项目类别: Alliance Grants
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=721879
• 关键词: Development; hybrid; quantum; classical; reservoir

This project will investigate the near-term viability and application of quantum computing to challenges in numerical simulation. The Principal Investigator, Dr. Zhangxing (John) Chen's extensive expertise in applying numerical techniques, and more recently machine learning, to reservoir simulation has greatly benefitted the energy industry, enabling economic and environmentally responsible utilization of resources, and enhancing the understanding of mathematical modelling in a variety of areas, such as geomechanics, computational fluid dynamics, and nanoparticles. Working with an expert in quantum computation, Dr. Barry Sanders, Dr. Chen will investigate the near-term application of quantum computing to reservoir simulation by decomposing non-quantum algorithms into modules, then assessing which quantum algorithms show potential starting points for development of practical implementations to accelerate these modules. This will determine the viability of different techniques, the required capacity of the quantum computing infrastructure and how long the quantum computation must run to solve a reservoir problem. Several quantum algorithms have been identified that may be suitable for integration into reservoir simulation software and the reservoir simulation workflow to provide performance improvements. This project will advance this preliminary research to proof-of-concept implementations that verify the viability of quantum computing for reservoir simulation. This will result in the design and validation of a hybrid quantum-classical accelerated reservoir simulator, that leverages both quantum and classical computing in an effective manner. Not only will this create a foundation for a system that can provide many benefits to the Canadian energy industry, it will also provide a template for integrating quantum computing into many other scientific disciplines, while providing direction and important reference work for the field of applied quantum computing.

该项目将研究量子计算在数值模拟中的挑战中的近期生存能力。 The Principal Investigator, Dr. Zhangxing (John) Chen's extensive expertise in applying numerical techniques, and more recently machine learning, to reservoir simulation has greatly benefitted the energy industry, enabling economic and environmentally responsible utilization of resources, and enhancing the understanding of mathematical modelling in a variety of areas, such as geomechanics, computational fluid dynamics, and nanoparticles. Chen博士与量子计算专家Barry Sanders博士合作，将通过将非量词算法分解为模块，然后评估哪种量子算法显示出潜在的开始实现的起点来加速这些模型。这将确定不同技术的生存能力，量子计算基础架构的所需能力以及量子计算必须运行的时间以解决储层问题。已经确定了几种量子算法，这些算法可能适合集成到储层仿真软件中，以及储层仿真工作流以提供性能改进。该项目将把这项初步研究推向概念验证实现，以验证量子计算用于储层模拟的可行性。这将导致设计和验证杂交量子经典的加速储层模拟器，该储层模拟器以有效的方式利用量子和经典计算。这不仅会为可以为加拿大能源行业提供许多好处的系统创造基础，还将提供一个模板，以将量子计算整合到许多其他科学学科中，同时为应用量子计算领域提供方向和重要参考工作。