Efficient Pause Location Prediction Using Quantum Annealing Simulations and Machine Learning

Despite increases in qubit count and connectivity in quantum annealers, a quantum speedup has yet to be observed for problems of practical significance. In order to further improve annealer performance, some researchers focus on tuning annealer parameters, such as the annealing schedule. In this work, we focus on pausing, an annealing schedule modification that has been shown to improve the probability of solving an optimization problem by orders of magnitude. However, a challenge associated with pausing is selecting an appropriate pause location, as pausing is only effective in problem-dependent regions and ineffective elsewhere. Moreover, there is little advice on how to determine where pausing is effective. Thus, pausing effectively is difficult and often inaccessible to the majority of users. To address these issues, we propose a data-driven method that leverages machine learning to predict optimal pause locations. First, we construct a dataset consisting of optimization problems and their corresponding optimal pause locations. The optimal pause locations are determined using spin-vector Monte Carlo, a method known to yield results similar to quantum annealing. Next, we train a convolutional neural network on this dataset, demonstrating its ability to distinguish between problem types and accurately predict the optimal pause location. Finally, we evaluate the model on multiple types of optimization problems. Our results show that the pause locations predicted by our method improve solution quality for all selected problem types. Additionally, our model can be pre-trained and easily distributed, making the power of pausing accessible to users unfamiliar with annealing schedule modifications.