A crucial step in the race towards quantum advantage is optimizing quantum annealing using ad-hoc annealing schedules. Motivated by recent progress in the field, we propose to employ long-short term memory (LSTM) neural networks to automate the search for optimal annealing schedules for random Ising models on regular graphs. By training our network using locally-adiabatic annealing paths, we are able to predict optimal annealing schedules for unseen instances and even larger graphs than those used for training.
@article{arxiv.2211.15209,
title = {Deep learning optimal quantum annealing schedules for random Ising models},
author = {Pratibha Raghupati Hegde and Gianluca Passarelli and Giovanni Cantele and Procolo Lucignano},
journal= {arXiv preprint arXiv:2211.15209},
year = {2023}
}