Quantum Gaussian Processes for computational design of materials

Gawel Kus

TU Delft

Recently, a quantum algorithm was proposed to accelerate Gaussian Processes regression, thus allowing to overcome the scaling issues inherent to this machine learning method. In our research, we implement this quantum algorithm and demonstrate its potential for enhancing the computational framework for data-driven design of materials. By simulation and numerical analysis, we expose (for the first time) the connection between the quantum Gaussian processes and the classical sparse approximations.