The effect of superposition and entanglement on hybrid quantum machine learning for weather forecasting

Abstract

Recently, proposed algorithms for quantum computing and generated quantum computer technologies continue to evolve. On the other hand, machine learning has become an essential method for solving many problems such as computer vision, natural language processing, prediction and classification. Quantum machine learning is a new field developed by combining the advantages of these two primary methods. As a hybrid approach to quantum and classical computing, variational quantum circuits are a form of machine learning that allows predicting an output value against input variables. In this study, the effects of superposition and entanglement on weather forecasting, were investigated using a variational quantum circuit model when the dataset size is small. The use of the entanglement layer between the variational layers has made significant improvements on the circuit performance. The use of the superposition layer before the data encoding layer resulted in the use of less variational layers.