Quantum Machine Learning hyperparameter search
This addresses hyperparameter tuning for machine learning practitioners, but it appears incremental as it applies quantum algorithms to an existing Fourier-series representation method.
The paper tackles hyperparameter optimization for machine learning models by developing a quantum-based Fourier-regression approach, which outperforms traditional methods in accuracy and convergence speed on an airline industry forecast benchmark.
This paper presents a quantum-based Fourier-regression approach for machine learning hyperparameter optimization applied to a benchmark of models trained on a dataset related to a forecast problem in the airline industry. Our approach utilizes the Fourier series method to represent the hyperparameter search space, which is then optimized using quantum algorithms to find the optimal set of hyperparameters for a given machine learning model. Our study evaluates the proposed method on a benchmark of models trained to predict a forecast problem in the airline industry using a standard HyperParameter Optimizer (HPO). The results show that our approach outperforms traditional hyperparameter optimization methods in terms of accuracy and convergence speed for the given search space. Our study provides a new direction for future research in quantum-based machine learning hyperparameter optimization.