CPS: Small: Infusing Quantum Computing, Decomposition, and Learning for Addressing Cyber-Physical Systems Optimization Challenges

CPS：小型：融合量子计算、分解和学习来应对网络物理系统优化挑战

• 批准号: 2312086
• 负责人: Amin Kargarian Marvasti
• 金额: $ 44.99万
• 依托单位: Louisiana State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312086&HistoricalAwards=false
• 关键词: CPS; Small; Infusing; Quantum; Computing

This NSF project aims to develop quantum computing-inspired algorithms for optimization problems that appear in various critical infrastructure systems, including power systems. The primary focus will be on computationally expensive mixed-integer optimization problems, which are hard to solve by classical computing machines. The project will bring transformative change in the ways that electric power system optimization problems are solved. This will be achieved by integrating machine learning and distributed computing with quantum computing techniques. The intellectual merits of the project include advancing the understanding of quantum computing optimization algorithms and harnessing their potential for solving complex problems. The broader impacts of the project include facilitating quantum computing applications for solving a variety of optimization problems, particularly for power systems operation and planning. In addition, multiple educational and outreach activities are envisioned to broaden the participation of under-resourced and underrepresented groups in computing and engineering, contribute to diverse workforce development, raise awareness of quantum technology among diverse audiences and children, and benefit local communities and the nation.This project will contribute to the field of cyber-physical systems optimization. Several existing technical and algorithmic challenges for solving optimization problems with quantum computing will be addressed. The project proposes the development of decomposition and distributed approaches, combined with quantum computing and machine learning, to create trainable quantum-classical algorithms for various mixed-binary optimization problems. The proposed strategies will enable the conversion of deterministic and stochastic mixed-binary problems in the power grid domain into multiple subproblems. These subproblems will be solvable by quantum computers in collaboration with classical computing machines. To achieve this, trainable variational quantum algorithms will be developed and integrated into intelligent quantum Lagrangian and Benders techniques. This integration will coordinate the computing operations performed by both quantum and classical machines.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该 NSF 项目旨在开发受量子计算启发的算法，用于解决包括电力系统在内的各种关键基础设施系统中出现的优化问题。主要关注点是计算量大的混合整数优化问题，这些问题很难用经典计算机来解决。该项目将为电力系统优化问题的解决方式带来革命性的改变。这将通过将机器学习和分布式计算与量子计算技术相结合来实现。该项目的智力优势包括增进对量子计算优化算法的理解并利用其解决复杂问题的潜力。该项目更广泛的影响包括促进量子计算应用解决各种优化问题，特别是电力系统的运行和规划。此外，预计将开展多项教育和外展活动，以扩大资源不足和代表性不足的群体对计算和工程的参与，促进多元化的劳动力发展，提高不同受众和儿童对量子技术的认识，并使当地社区和国家受益.该项目将为网络物理系统优化领域做出贡献。将解决利用量子计算解决优化问题的几个现有技术和算法挑战。该项目提出开发分解和分布式方法，结合量子计算和机器学习，为各种混合二进制优化问题创建可训练的量子经典算法。所提出的策略将能够将电网领域中的确定性和随机混合二元问题转换为多个子问题。这些子问题将可以通过量子计算机与经典计算机的协作来解决。为了实现这一目标，将开发可训练的变分量子算法并将其集成到智能量子拉格朗日和本德斯技术中。这种集成将协调量子机器和经典机器执行的计算操作。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。