  The meeting of artificial intelligence (AI) and quantum computing is already
a reality; quantum machine learning (QML) promises the design of better
regression models. In this work, we extend our previous studies of materials
discovery using classical active learning (AL), which showed remarkable economy
of data, to explore the use of quantum algorithms within the AL framework (QAL)
as implemented in the MLChem4D and QMLMaterials codes. The proposed QAL uses
quantum support vector regressor (QSVR) or a quantum Gaussian process regressor
(QGPR) with various quantum kernels and different feature maps. Data sets
include perovskite properties (piezoelectric coefficient, band gap, energy
storage) and the structure optimization of a doped nanoparticle (3Al@Si11)
chosen to compare with classical AL results. Our results revealed that the QAL
method improved the searches in most cases, but not all, seemingly correlated
with the roughness of the data. QAL has the potential of finding optimum
solutions, within chemical space, in materials science and elsewhere in
chemistry.
