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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=707354
• 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应对数值模拟挑战的近期可行性和应用。首席研究员陈章兴博士在将数值技术和最近的机器学习应用于油藏模拟方面拥有丰富的专业知识，使能源行业受益匪浅，实现了资源的经济和环境负责任的利用，并增强了对数学模型的理解在地质力学、计算流体动力学和纳米粒子等各个领域。陈博士将与量子计算专家 Barry Sanders 博士合作，通过将非量子算法分解为模块，研究量子计算在油藏模拟中的近期应用，然后评估哪些量子算法显示出开发油藏模拟的潜在起点。加速这些模块的实际实现。这将决定不同技术的可行性、量子计算基础设施所需的容量以及量子计算必须运行多长时间才能解决水库问题。已经确定了几种可能适合集成到油藏模拟软件和油藏模拟工作流程中以提供性能改进的量子算法。该项目将把这项初步研究推进到概念验证实施，以验证量子计算在油藏模拟中的可行性。这将导致混合量子经典加速油藏模拟器的设计和验证，该模拟器以有效的方式利用量子和经典计算。这不仅将为加拿大能源行业带来诸多好处的系统奠定基础，还将提供将量子计算融入许多其他科学学科的模板，同时为应用量子领域提供方向和重要参考工作。计算。