To accelerate the process of materials design, materials science has increasingly used data driven techniques to extract information from collected data. Specially, machine learning (ML) algorithms, which span the ML discipline, have demonstrated ability to predict various properties of materials with the level of accuracy similar to explicit calculation of quantum mechanical theories, but with significantly reduced run time and computational resources. Within ML, graph neural networks have emerged as an important algorithm within the field of machine learning, since they are capable of predicting accurately a wide range of important physical, chemical and electronic properties due to their higher learning ability based on the graph representation of material and molecular descriptors through the aggregation of information embedded within the graph. In parallel with the development of state of the art classical machine learning applications, the fusion of quantum computing and machine learning have created a new paradigm where classical machine learning model can be augmented with quantum layers which are able to encode high dimensional data more efficiently. Leveraging the structure of existing algorithms, we developed a unique and novel gradient free hybrid quantum classical convoluted graph neural network (HyQCGNN) to predict formation energies of perovskite materials. The performance of our hybrid statistical model is competitive with the results obtained purely from a classical convoluted graph neural network, and other classical machine learning algorithms, such as XGBoost. Consequently, our study suggests a new pathway to explore how quantum feature encoding and parametric quantum circuits can yield drastic improvements of complex ML algorithm like graph neural network.

翻译：为加速材料设计过程，材料科学日益采用数据驱动技术从收集的数据中提取信息。尤其是机器学习（ML）算法——涵盖整个机器学习学科——已展现出预测材料各种属性的能力，其准确度与量子力学理论的显式计算相当，但运行时间和计算资源显著减少。在机器学习领域，图神经网络已成为重要算法，因其基于材料和分子描述符的图表示（通过聚合图内嵌入的信息）具有更强的学习能力，能够准确预测一系列重要的物理、化学和电子属性。在与最先进经典机器学习应用发展的同时，量子计算与机器学习的融合催生了一种新范式：经典机器学习模型可通过添加量子层进行增强，这些量子层能够更高效地编码高维数据。利用现有算法结构，我们开发了一种独特新颖的无梯度混合量子经典卷积图神经网络（HyQCGNN），用于预测钙钛矿材料的形成能。我们的混合统计模型性能与纯经典卷积图神经网络及其他经典机器学习算法（如XGBoost）的结果相当。因此，本研究探索了一条新路径，以揭示量子特征编码和参数化量子电路如何能够显著改进图神经网络等复杂机器学习算法。