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arXiv · 2211.15209

Deep learning optimal quantum annealing schedules for random Ising models

Pratibha Raghupati Hegde·Gianluca Passarelli·Giovanni Cantele·Procolo Lucignano
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Abstract

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.

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Pratibha Raghupati Hegde, Gianluca Passarelli, Giovanni Cantele, Procolo Lucignano. 2022-11-28. Deep learning optimal quantum annealing schedules for random Ising models. https://doi.org/10.1088/1367-2630%2Face547

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