ERI: Variational Quantum Algorithm for Power System Simulation

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助量子计算已被证明在解决许多问题方面比经典计算机具有指数优势。仅使用量子计算机实现指数优势可能需要数十年的研究，因为它需要具有数千个量子位和长相干时间的通用容错量子计算机。变分量子算法（VQA）作为量子和经典算法的混合算法，在许多问题上表现出了比经典算法更大的优势。提高大容量电力系统的安全性和可靠性可以节省数十亿美元的经济损失。为了确保电力系统的可靠性和安全性，暂态稳定仿真和事故分析是非常频繁地执行的基本任务（即使是真实的时间，即，解决方案需要在不到一分钟的时间内产生）。大型电力系统的暂态稳定仿真和事故分析，许多发电机，变压器和输电线路是非常具有挑战性的问题，由于非常高的维数。现有的工具基于经典的计算机有很大的困难，在执行这样的应急分析的大型电力系统，特别是当考虑到多个组件的同时故障。由于电力、天然气和通信系统之间的相互依赖性以及大量可再生发电机被集成到电力系统中的事实，导致维度的显著增加，使困难进一步复杂化。暂态稳定仿真和事故分析本质上是用微分代数方程（DAE）来表示的，微分代数方程也可以表示许多其他的工程问题。通常，经典算法在求解非常高维的DAE时具有难以处理的计算负担。该项目提出了新的VQA，以解决DAE作为一种新的范例。更广泛的影响活动包括：（a）传播研究成果，激励电力和能源界加快量子计算的研究和开发，以解决具有挑战性的工程问题;（B）加强阿尔弗雷德大学量子计算和电力系统的课程;（c）让代表性不足的群体的学生和本科生参与研究，以及（d）通过外展活动教育公众和K-12。拟议工作的目标是开发新的VQA，可以有效地解决非常高维的模拟问题（例如，暂态稳定性和N-k应急分析）的大型电力系统与可再生能源，这将是第一次。办法是：1）开发一般时间依赖非线性微分方程的VQA，其中两个核心组件（2）开发了用于电力系统暂态稳定仿真的哈密顿量和量子非线性处理单元（QNPU），QNPU是VQA的第三个核心部件，3）开发可再生能源高渗透率电力系统N-k事故分析的VQA。几年后，当拥有几百个量子比特的嘈杂的中等规模量子计算机问世时，从这个项目中开发的VQA预计将实现指数优势，超过纯经典算法，以解决暂态稳定仿真和N-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。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2)Quantum computing has been proven to have an exponential advantage over classical computers in solving many problems. Realizing the exponential advantage using only a quantum computer will potentially take decades of research as it needs a universal fault-tolerant quantum computer with thousands of qubits and long coherence time. Variational quantum algorithms (VQAs), as hybrid quantum and classical algorithms, have shown an exponential advantage over classical algorithms for various problems. Enhancing the security and reliability of bulk power systems can save billions of economic losses. To ensure power system reliability and security, transient stability simulation and contingency analysis are essential tasks that are executed very frequently (even in real time, i.e., a solution needs to be produced in less than one minute) at utilities and independent system operators. Transient stability simulation and contingency analysis for a large power system with many generators, transformers, and transmission lines are extremely challenging problems due to the very-high dimensionality. Existing tools based on classical computers have great difficulty in performing such contingency analysis for a large power system, especially when considering the simultaneous failure of multiple components. The difficulty is further complicated by the significant increase in dimensionality caused by the interdependency between power, gas, and communication systems and by the fact that a significant number of renewable generators are being integrated into power systems. Transient stability simulation and contingency analysis are essentially modeled as differential algebraic equations (DAEs) which also can represent many other engineering problems. In general, classical algorithms have an intractable computational burden for solving very-high-dimensional DAEs. This project proposes new VQAs to solve DAEs as a new paradigm. Broader impact activities include (a) disseminating research results to inspire the power and energy community to accelerate research and development in quantum computing for challenging engineering problems, (b) curriculum enhancement on quantum computing and power systems at Alfred University, (c) involving students from underrepresented groups and undergraduates in research, and (d) educating the public and K-12 through outreach activities.The goal of the proposed work is to develop new VQAs that can efficiently solve time-dependent nonlinear differential equations for very-high-dimensional simulation problems (e.g., transient stability and N-k contingency analysis) of large-scale power systems with renewables, which will be the first of its kind. The approach is to: 1) develop VQAs for general time-dependent nonlinear differential equations, where two core components (variational ansatz and optimization algorithm) of a VQA will be comprehensively investigated, 2) develop Hamiltonian and quantum nonlinear processing unit (QNPU) for power system transient stability simulation, where QNPU is the third core component of the VQA, 3) develop VQAs for N-k contingency analysis of power systems with high-penetration renewables. When noisy intermediate-scale quantum computers with a few hundred qubits are available in several years, the VQAs developed from this project are expected to realize exponential advantage over pure classical algorithms to solve transient stability simulation and N-k contingency analysis problems of remarkable significance.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.