Reducing hyperparameter sensitivity in measurement-feedback based Ising machines
This work addresses the practical challenge of hyperparameter tuning for researchers and engineers developing measurement-feedback based analog Ising machines, which are hardware solvers for combinatorial optimization.
This paper investigates the discrepancy in effective hyperparameter ranges between time-continuous and time-discrete measurement-feedback based analog Ising machines. They propose and experimentally verify a method to reduce hyperparameter sensitivity in these measurement-feedback architectures.
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.