Early Exploration of a Flexible Framework for Efficient Quantum Linear Solvers in Power Systems
arxiv(2024)
Abstract
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.
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