Topologically Defective Lattice Potential-Based Gain-Dissipative Ising Annealer with Large Noise Margin

Abstract

Gain-dissipative Ising machines (GIMs) are annealers inspired by physical systems such as Ising spin glasses to solve combinatorial optimization problems. Compared to traditional quantum annealers, GIM is relatively easier to scale and can save on additional power consumption caused by low-temperature cooling. However, traditional GIMs have a limited noise margin. Specifically, their normal operation requires ensuring that the noise intensity is lower than their saturation fixed point amplitude, which may result in increased power consumption to suppress noise-induced spin state switching. To enhance the noise robustness of GIM, in this study a GIM based on a topologically defective lattice potential (TDLP) is proposed. Numerical simulations demonstrate that the TDLP-based GIM can accurately simulate the bifurcation spin evolution in the Ising model. Furthermore, through the MAXCUT benchmark based on G-set graphs, the optimal performance of TDLP-based GIM is shown to surpass that of traditional GIMs. Additionally, the proposed TDLP-based GIM successfully solves the MAXCUT benchmark and domain clustering dynamics benchmark based on G-set graphs when the noise intensity exceeds its saturation fixed-point amplitude. This indicates that the proposed system provides a promising architecture for breaking the small noise constraints required by traditional GIMs.