@misc{KasebMollerSpoorGuoXiangPalenskyVergara2025a,
  doi = {10.48550/ARXIV.2511.20237},
  url = {https://arxiv.org/abs/2511.20237},
  author = {Kaseb,  Zeynab and Moller,  Matthias and Spoor,  Lindsay and Guo,  Jerry J. and Xiang,  Yu and Palensky,  Peter and Vergara,  Pedro P.},
  keywords = {Systems and Control (eess.SY),  Emerging Technologies (cs.ET),  Machine Learning (cs.LG),  FOS: Electrical engineering,  electronic engineering,  information engineering,  FOS: Electrical engineering,  electronic engineering,  information engineering,  FOS: Computer and information sciences,  FOS: Computer and information sciences},
  title = {Quantum-Enhanced Reinforcement Learning for Accelerating Newton-Raphson Convergence with Ising Machines: A Case Study for Power Flow Analysis},
  publisher = {arXiv},
  year = {2025},
  copyright = {Creative Commons Attribution Non Commercial No Derivatives 4.0 International}
}