Remote sensing of urbanization using machine learning and variational quantum regression.

Urbanization plays an important role in the field of environmental science and for the growth of green society. Urbanization scaled up with the economical benefits in developing countries and in countries making sustainable development process on ecological paths with higher efficiency on the growth of the ecosystem. In civilization due to urbanization there are problems such as air excellence corrosion, limitations on the space for survival, issues in the health of urban. Towards the perspective advancement of urban ecology it is distorted from a speculative study in the field of interdisciplinary. This article provides a new idea using Machine Learning and Quantum Computing to produce an output where it gives an efficient building idea of integrating plants and trees with or around the building. The dataset are emission and absorption of carbon dioxide. In order to create the X_features, we either use approximation factors or real-time remote sensing. The main goal of the system is to train them with different machine learning models and show that if the data set time complexity increases exponentially, Quantum Computers can be used to run the models. [ABSTRACT FROM AUTHOR]