A quantum active learning workflow using quantum Gaussian process regression with projected and fidelity quantum kernels finds the global minimum of 4Al@Si11, but with no clear advantage over classical active learning and a search budget covering most of the isomer space.
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Quantum Active Learning for Structural Determination of Doped Nanoparticles -- a Case Study of 4Al@Si$_{11}$
A quantum active learning workflow using quantum Gaussian process regression with projected and fidelity quantum kernels finds the global minimum of 4Al@Si11, but with no clear advantage over classical active learning and a search budget covering most of the isomer space.