标题： 量子主动学习用于掺杂纳米颗粒的结构确定——4Al@Si$_{11}$的案例研究 显示英文标题

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

摘要： 主动学习（AL）已在化学和材料科学中得到广泛应用。 在本工作中，我们提出了一种量子主动学习（QAL）方法，用于掺杂纳米颗粒的自动结构确定，其中迭代使用量子机器学习（QML）回归模型来指示通过DFT或DFTB计算的新结构，此新数据获取用于重新训练QML模型。 QAL方法在量子机器学习软件/代理材料设计与发现（QMLMaterial）中实现，其目的是使用由QML回归算法定义的人工代理，选择下一个要计算的掺杂构型，该构型找到最优结构的概率更高。 QAL使用带有保真度量子核的量子高斯过程以及投影量子核和不同的量子电路。 为了进行比较，使用了具有不同经典核的经典AL。 所提出的QAL方法应用于掺杂Si$_{11}$的结构确定，其中含有4个Al（4Al@Si$_{11}$），结果表明QAL方法能够找到最优的4Al@Si$_{11}$结构。 本工作的目的是展示QAL方法——在无噪声量子计算框架中制定——用于掺杂纳米颗粒和材料缺陷的自动结构确定。 显示英文摘要

摘要： 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.