TY  - JOUR
PB  - SciPost Foundation
DO  - 10.21468/SciPostPhys.15.1.018
TI  - Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning
PY  - 2023/07/20
UR  - https://scipost.org/SciPostPhys.15.1.018
JF  - SciPost Physics
JA  - SciPost Phys.
VL  - 15
IS  - 1
SP  - 018
A1  - Scriva, Giuseppe
AU  - Costa, Emanuele
AU  - McNaughton, Benjamin
AU  - Pilati, Sebastiano
AB  - 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.
ER  -

@Article{10.21468/SciPostPhys.15.1.018,
	title={{Accelerating equilibrium spin-glass simulations using quantum annealers via generative deep learning}},
	author={Giuseppe Scriva and Emanuele Costa and Benjamin McNaughton and Sebastiano Pilati},
	journal={SciPost Phys.},
	volume={15},
	pages={018},
	year={2023},
	publisher={SciPost},
	doi={10.21468/SciPostPhys.15.1.018},
	url={https://scipost.org/10.21468/SciPostPhys.15.1.018},
}