Deep learning optimal quantum annealing schedules for random Ising models

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.

10 pages, 8 figures

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Cited by in corpus (6)

• A comprehensive review on developments of synthetic dimensions
• Designing Quantum Annealing Schedules using Bayesian Optimization
• Diabatic Quantum Annealing for the Frustrated Ring Model
• Convergence of Digitized-Counterdiabatic QAOA: circuit depth versus free parameters
• Noise Effects on Diabatic Quantum Annealing Protocols
• Hidden local adiabatic ramp in the modulated time evolution and the quantum approximate optimization algorithm