Quantum Analog Annealing of Gain‐Dissipative Ising Machine Driven by Colored Gaussian Noise

• Published in: Advanced theory and simulations 2022-03, Vol.5 (3), p.n/a
• Main Authors: Liao, Zhiqiang, Ma, Kaijie, Sarker, Md Shamim, Tang, Siyi, Yamahara, Hiroyasu, Seki, Munetoshi, Tabata, Hitoshi
• Format: Article
• Language: English
• Subjects: colored Gaussian noise; combinatorial optimization; gain‐dissipative system; Ising machine; quantum analog annealing
• ISSN: 2513-0390; 2513-0390
• DOI: 10.1002/adts.202100497

Gain‐dissipative Ising machines (GIMs) are a type of quantum analog equipment that can rapidly determine the optimal solution for combinatorial optimization problems. When the noise intensity is significantly lower than the fixed point of the system, the performance of a GIM is not influenced by the fluctuation of the noise intensity. However, the noise in this study is limited to Gaussian white noise. The influence of prevalent colored noise on GIMs has not been researched. In this study, the influence of common‐colored noise on the performance of GIMs is numerically investigated. The results of a domain clustering dynamics analysis reveal that red noise can better suppress the generation of the noise‐induced irregular temporary domain. Furthermore, several prevalent MAXCUT problem topologies, including the Moebius ladder, random Moebius ladder, and 2D random lattice, are adopted as test benchmarks. The results reveal that GIMs influenced by white, blue, and violet noise perform better at low‐intensity noise condition. In contrast, pink and red noise‐injected GIMs demonstrate higher performances when applied to MAXCUT topologies with both ferromagnetic and antiferromagnetic connections under larger noise intensity conditions. This indicates that the noise dispersion can be used as an additional hyperparameter to optimize the performance of GIMs. To understand the effect of the ubiquitous colored noise on the gain‐dissipative Ising machines, this work numerically investigates the performance of gain‐dissipative Ising machines with different noise dispersions on several typical benchmarks. The results reveal that the noise dispersion can be tuned as a hyperparameter to enhance the success rate of the Ising machine for solving NP‐hard problems.