Quantum Active Learning for Structural Determination of Doped Nanoparticles -- a Case Study of 4Al@Si$_{11}$

TL;DR

This paper introduces a quantum active learning method that uses quantum machine learning models to efficiently determine the optimal structure of doped nanoparticles, demonstrated on 4Al@Si$_{11}$, improving structural prediction accuracy.

Contribution

The paper presents a novel quantum active learning approach utilizing quantum kernels and circuits for structural determination, advancing beyond classical methods.

Findings

QAL successfully identified the optimal 4Al@Si$_{11}$ structure.

Quantum kernels improved the efficiency of structure prediction.

The method operates within a noise-free quantum computing framework.

Abstract

Active learning (AL) has been widely applied in chemistry and materials science. In this work we propose a quantum active learning (QAL) method for automatic structural determination of doped nanoparticles, where quantum machine learning (QML) models for regression are used iteratively to indicate new structures to be calculated by DFT or DFTB and this new data acquisition is used to retrain the QML models. The QAL method is implemented in the Quantum Machine Learning Software/Agent for Material Design and Discovery (QMLMaterial), whose aim is using an artificial agent (defined by QML regression algorithms) that chooses the next doped configuration to be calculated that has a higher probability of finding the optimum structure. The QAL uses a quantum Gaussian process with a fidelity quantum kernel as well as the projected quantum kernel and different quantum circuits. For comparison, classical AL was used with a classical Gaussian process with different classical kernels. The presented QAL method was applied in the structural determination of doped Si$_{11}$ with 4 Al (4Al@Si$_{11}$) and the results indicate the QAL method is able to find the optimum 4Al@Si$_{11}$ structure. The aim of this work is to present the QAL method -- formulated in a noise-free quantum computing framework -- for automatic structural determination of doped nanoparticles and materials defects.