Quantum Gaussian Process

Quantum Gaussian processes leverage quantum computing to accelerate Gaussian process regression, a Bayesian machine learning technique used for function approximation and optimization. Current research focuses on developing quantum algorithms, such as those employing quantum principal component analysis and parameterized quantum circuits, to improve the scalability and efficiency of Gaussian process methods, particularly within Bayesian optimization frameworks. This work aims to overcome the computational limitations of classical Gaussian processes, potentially leading to faster and more powerful machine learning models for various applications, including hyperparameter tuning and black-box function optimization. Early results suggest potential speedups compared to classical approaches, although further research is needed to fully assess practical advantages.