Quantum machine learning is a relatively new research field that aims to combine the dramatic performance advantage offered by quantum computing and the ability of machine learning algorithms to learn complex distributions of high-dimensional data. The primary focus of this domain is the implementation of classical machine learning algorithms in the quantum mechanical domain and study of the speedup due to quantum parallelism, which could enable the development of novel techniques for solving problems such as quantum phase recognition and quantum error correction optimization. In this paper, we propose a hybrid quantum machine learning pipeline for predicting the atomization energies of various molecules using the nuclear charges and atomic positions of the constituent atoms. Firstly, we will be using a deep convolutional auto-encoder model for the feature extraction of data constructed from the eigenvalues and eigenvector centralities of the pairwise distance matrix calculated from atomic positions and the unrolled upper triangle of each Coulomb matrix calculated from nuclear charges, and we will then be using a quantum regression algorithm such as quantum linear regression, quantum radial basis function neural network and, a quantum neural network for estimating the atomization energy. The hybrid quantum neural network models do not seem to provide any speedup over their classical counterparts. Before implementing a quantum algorithm, we will also be using state-of-the-art classical machine learning and deep learning models such as XGBoost, multilayer perceptron, deep convolutional neural network, and a long short-term memory network to study the correlation between the extracted features and corresponding atomization energies of molecules.

量子机器学习是一个相对较新的研究领域，旨在将量子计算所提供的显着性能优势与机器学习算法学习高维数据的复杂分布的能力相结合。该领域的主要重点是在量子力学领域中实施经典的机器学习算法，以及研究由于量子并行性而产生的加速，这可能使开发解决诸如量子相位识别和量子纠错优化等问题的新技术成为可能。 。在本文中，我们提出了一种混合量子机器学习流水线，用于利用组成原子的核电荷和原子位置来预测各种分子的雾化能。首先，我们将使用深度卷积自动编码器模型对从原子位置计算出的成对距离矩阵的特征值和特征向量中心以及从核电荷计算出的每个库仑矩阵的展开上三角形构成的数据进行特征提取，然后使用量子回归算法，例如量子线性回归，量子径向基函数神经网络和用于估计雾化能量的量子神经网络。混合量子神经网络模型似乎没有提供比传统模型更快的速度。在实施量子算法之前，我们还将使用最先进的经典机器学习和深度学习模型，例如XGBoost，多层感知器，深度卷积神经网络，