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.

利用数据驱动技术的材料设计过程中，我们开发了一种融合经典和量子计算的混合模型来预测钙钛矿材料的能量形成。该模型的性能与经典模型和其他机器学习算法相媲美，为探索量子特征编码和参数化量子电路在图神经网络等复杂机器学习算法中的巨大改进提供了一种新途径。