面向电力系统高效量子线性求解器的灵活框架的早期探索
Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems
AI总结：
针对电力系统大规模线性方程组求解的经典计算局限及HHL算法电路深度不适配NISQ设备的问题，提出由NWQSim驱动的通用框架，通过门融合、降电路深度与GPU加速实现最高8倍于Qiskit Aer的加速且精度相当。
摘要：
可再生能源的快速整合给电网管理带来了巨大挑战。尽管先进计算与机器学习技术为加速电网建模仿真提供了部分解决方案，但仍存在经典计算机无法有效解决的复杂问题。量子计算作为一项前景广阔的技术，有望从根本上改变电力系统的管理方式，尤其是在可再生能源占比更高的场景中。其中一个关键方向是求解大规模线性方程组，这对潮流分析等电力系统应用至关重要，而Harrow-Hassidim-Lloyd（HHL）算法是知名的量子求解方案。但HHL量子电路往往深度过大，无法适用于当前的含噪声中等规模量子（Noisy-Intermediate-Scale-Quantum, NISQ）设备。本文提出了一个由NWQSim驱动的通用框架，搭建起电力系统应用与Qiskit中可用的量子线性求解器之间的桥梁。该框架可助力研究人员用量子线性求解器高效探索电力系统应用。通过创新的门融合策略、降低的电路深度以及GPU加速，我们的模拟器显著提升了资源效率。潮流案例研究表明，与Qiskit Aer相比，该框架最高可实现8倍加速，同时保持相当的精度水平。
英文摘要：
The rapid integration of renewable energy resources presents formidable challenges in managing power grids. While advanced computing and machine learning techniques offer some solutions for accelerating grid modeling and simulation, there remain complex problems that classical computers cannot effectively address. Quantum computing, a promising technology, has the potential to fundamentally transform how we manage power systems, especially in scenarios with a higher proportion of renewable energy sources. One critical aspect is solving large-scale linear systems of equations, crucial for power system applications like power flow analysis, for which the Harrow-Hassidim-Lloyd (HHL) algorithm is a well-known quantum solution. However, HHL quantum circuits often exhibit excessive depth, making them impractical for current Noisy-Intermediate-Scale-Quantum (NISQ) devices. In this paper, we introduce a versatile framework, powered by NWQSim, that bridges the gap between power system applications and quantum linear solvers available in Qiskit. This framework empowers researchers to efficiently explore power system applications using quantum linear solvers. Through innovative gate fusion strategies, reduced circuit depth, and GPU acceleration, our simulator significantly enhances resource efficiency. Power flow case studies have demonstrated up to a eight-fold speedup compared to Qiskit Aer, all while maintaining comparable levels of accuracy.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
