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

Abstract

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.

A Multi-Objective Hybrid Game Pricing Strategy for Integrated Energy Operator-Load Aggregator Alliances Considering Integrated Demand Response

“…In future research, the algorithm proposed here can be combined with simulated annealing optimizers, which have already been widely used in multi-objective models, to achieve better solution performance. References [38] and [39] provide more information on this topic.…”

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

“…At this point, the TDLP-based GIM reaches the ground state of a 100-spin FSL at a speed similar to traditional GIMs with similar saturated fixed point amplitudes when 𝛾 = 0.01. [13] This demonstrates the stronger noise damping effect of TDLP compared to the nonlinearity of traditional GIM. In addition, once the system reaches a steady state, it hardly generates temporary domains that destroy the clustered domains, as shown in Figure 7e,f.…”

“…Hence, the generation of irregular temporary domains can be solely attributed to thermal perturbations. [25] As validated in the CIM, [13,48] Metropolis-Hastings algorithm, [25] and quantum annealing hardware, [48] the effective temperature variations of the optimizer can be estimated according to the generation of irregular temporary domains.…”

“…GIMs typically iteratively search for the global optimum of COPs through the interaction of linear gain dynamics and nonlinear bifurcation dynamics. [13] This process can be time-efficient, even surpassing the time requirements of quantum annealing. [14] GIMs have been successfully implemented and demonstrated outstanding performance in physical systems such as field programmable gate arrays, [15,16] optoelectronics, [17,18] spintronics, [19,20] and electromechanics.…”