Solving NP-hard problems with bistable polaritonic networks
Mesoscale and Nanoscale Physics
2019-05-15 v1 Disordered Systems and Neural Networks
Quantum Physics
Abstract
A lattice of locally bistable driven-dissipative cavity polaritons is found theoretically to effectively simulate the Ising model, also enabling an effective transverse field. We benchmark the system performance for spin glass problems, and study the scaling of the ground state energy deviation and success probability as a function of system size. As particular examples we consider NP-hard problems embedded in the Ising model, namely graph partitioning and the knapsack problem. We find that locally bistable polariton networks act as classical simulators for solving optimization problems, which can potentially present an improvement within the exponential complexity class.
Keywords
Cite
@article{arxiv.1808.00081,
title = {Solving NP-hard problems with bistable polaritonic networks},
author = {O. Kyriienko and H. Sigurdsson and T. C. H. Liew},
journal= {arXiv preprint arXiv:1808.00081},
year = {2019}
}
Comments
6+5 pages, 5+8 figures