2020/07/31 by Stella L. Harrison, Helgi Sigurðsson, Helgi Sigurdsson +3 · 1 citation
Computer Science · Mathematics · Physics and Astronomy · #Algorithm #Binary number #Coherent states #Combinatorics #Computer science #Discretization #Hamiltonian (control theory) #Hyperplane #Ising model #Mathematical analysis #Mathematical optimization #Mathematics #Neural Networks and Reservoir Computing #Physics #Quantum #Quantum Computing Algorithms and Architecture #Quantum Information and Cryptography #Quantum mechanics #Simulated annealing #Statistical physics #Ternary operation #Topology (electrical circuits) #cond-mat.mes-hall #quant-ph
paper · pdf · doi:10.1103/physrevapplied.17.024063
published as Phys. Rev. Applied 17, 024063 (Feb. 2022) · 12 pages, 8 figures
openalex publication_date 2022/02/24 · openalex created_date 2022/03/02 · arxiv created 2022/03/07 · arxiv updated 2022/03/08 · openalex updated_date 2026/07/28
Many computational problems are intractable through classical computing and, as Moore's law is drawing to a halt, demand for finding alternative methods in tackling these problems is growing. Here, we realize a liquid light machine for the NP-hard max-3-cut problem based on a network of synchronized exciton-polariton condensates. We overcome the binary limitation of the decision variables in Ising machines using the continuous-phase degrees of freedom of a coherent network of polariton condensates. The condensate network dynamical transients provide optically-fast annealing of the XY Hamiltonian. We apply the Goemans and Williamson random hyperplane technique, discretizing the XY ground state spin configuration to serve as ternary decision variables for an approximate optimal solution to the max-3-cut problem. Applications of the presented coherent network are investigated in image-segmentation tasks and in circuit design.