Potts model solver based on hybrid physical and digital architecture
Kensuke Inaba, Takahiro Inagaki, Koji Igarashi, Shoko Utsunomiya, Toshimori Honjo, Takuya Ikuta, Koji Enbutsu, Takeshi Umeki, Ryoichi Kasahara, Kyo Inoue, Y. Yamamoto, Hiroki Takesue
Abstract
Abstract The Potts model describes Ising-model-like interacting spin systems with multivalued spin components, and ground-state search problems of the Potts model can be efficiently mapped onto various integer optimization problems thanks to the rich expression of the multivalued spins. Here, we demonstrate a solver of this model based on hybrid computation using physical and digital architectures, wherein a digital computer updates the interaction matrices in the iterative calculations of the physical Ising-model solvers. This update of interactions corresponds to learning from the Ising solutions, which allows us to save resources when embedding a problem in a physical system. We experimentally solved integer optimization problems (graph coloring and graph clustering) with this hybrid architecture in which the physical solver consisted of coupled degenerate optical parametric oscillators.