Quantum computing, leveraging principles of quantum mechanics, represents a
transformative approach in computational methodologies, offering significant
enhancements over traditional classical systems. This study tackles the complex
and computationally demanding task of simulating power system transients
through solving differential algebraic equations (DAEs). We introduce two novel
Quantum Neural Networks (QNNs): the Sinusoidal-Friendly QNN and the
Polynomial-Friendly QNN, proposing them as effective alternatives to
conventional simulation techniques. Our application of these QNNs successfully
simulates two small power systems, demonstrating their potential to achieve
good accuracy. We further explore various configurations, including time
intervals, training points, and the selection of classical optimizers, to
optimize the solving of DAEs using QNNs. This research not only marks a
pioneering effort in applying quantum computing to power system simulations but
also expands the potential of quantum technologies in addressing intricate
engineering challenges.

本研究旨在应用量子计算技术解决电力系统仿真问题，通过引入两种新型量子神经网络（Sinusoidal-Friendly QNN 和 Polynomial-Friendly QNN）来优化解决微分代数方程的传统模拟技术，并成功地在两个小型电力系统中应用这些量子神经网络进行仿真，表明其在精度上具有潜力，且进一步研究了各种配置来优化量子神经网络解决微分代数方程的效果，这不仅是量子计算在电力系统模拟中的首次尝试，还拓展了量子技术在解决复杂工程问题上的潜力。