Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning

Abstract

Adiabatic quantum computers, such as the quantum annealers commercialized by D-Wave Systems Inc., are routinely used to tackle combinatorial optimization problems. In this article, we show how to exploit them to accelerate equilibrium Markov chain Monte Carlo simulations of computationally challenging spin-glass models at low but finite temperatures. This is achieved by training generative neural networks on data produced by a D-Wave quantum annealer, and then using them to generate smart proposals for the Metropolis-Hastings algorithm. In particular, we explore hybrid schemes by combining single spin-flip and neural proposals, as well as D-Wave and classical Monte Carlo training data. The hybrid algorithm outperforms the single spin-flip Metropolis-Hastings algorithm. It is competitive with parallel tempering in terms of correlation times, with the significant benefit of a much shorter equilibration time.

24 pages, 7 figures

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• Minimalist Neural Networks training for phase classification in diluted-Ising models
• Neural-network-assisted Monte Carlo sampling trained by Quantum Approximate Optimization Algorithm
• Challenges and opportunities in the supervised learning of quantum circuit outputs