Analog Ising machines have been proposed as heuristic hardware solvers for combinatorial optimization problems, with the potential to outperform conventional approaches, provided that their hyperparameters are carefully tuned. Their temporal evolution is often described using time-continuous dynamics. However, most experimental implementations rely on measurement-feedback architectures that operate in a time-discrete manner. We observe that in such setups, the range of effective hyperparameters is substantially smaller than in the envisioned time-continuous analog Ising machine. In this paper, we analyze this discrepancy and discuss its impact on the practical operation of Ising machines. Next, we propose and experimentally verify a method to reduce the sensitivity to hyperparameter selection of these measurement-feedback architectures.
Black-box optimization for integer-variable problems using Ising machines and factorization machines
Keio University（庆应义塾大学）；National Institute for Materials Science（国立材料科学研究所）；The University of Tokyo（东京大学）；Waseda University（早稻田大学）；Tokyo Institute of Technology（东京工业大学）
Incorporating rank-free coupling and external field via an incoherent modulated spatial photonic Ising machine
State Key Laboratory of Photonics and Communications（光子与通信国家重点实验室）；Institute for Quantum Sensing and Information Processing（量子传感与信息处理研究所）；Shanghai Jiao Tong University（上海交通大学）；Hefei National Laboratory（合肥国家实验室）；Shanghai Research Center for Quantum Sciences（上海量子科学研究中心）
Breaking the Tuning Barrier: Zero-Hyperparameters Yield Multi-Corner Analysis Via Learned Priors
School of Mathematical and Physical Science, University of Sheffield（谢菲尔德大学数学与物理科学学院）；SZU–UoS Joint Centre for Innovation and Entrepreneurship, College of Mechatronics and Control Engineering, Shenzhen University（深大-乌兹别克斯坦联合创新与创业中心，机电控制工程学院，深圳大学）；Nanjing University of Science and Technology（南京理工大学）
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization
Argonne National Laboratory（阿贡国家实验室）；Parallel Works Inc.（Parallel Works 公司）；Convergent Science, Inc.（Convergent Science 公司）
From Entity Reliability to Clean Feedback: An Entity-Aware Denoising Framework Beyond Interaction-Level Signals
University of Science；Technology of China \& State Key Laboratory of Cognitive Intelligence China；Technology of China \& State Key Laboratory of Cognitive Intelligence
