Quantum Annealers for Low-Temperature Boltzmann Monte Carlo Simulations

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

We show how to exploit quantum annealers to accelerate equilibrium markov chain monte carlo simulations of spin glasses at low temperature.In particular, we explore hybrid schemes by combining single spin-flip and neuralproposals, as well as classical and quantum annealer training data.The hybrid algorithm outperforms the single spin-flip metropolis-hastings algorithm in terms of correlation times, with the significant benefit of a much faster equilibration.