A Quantum Machine Learning Approach to Spatiotemporal Emission Modelling is a research article published by MDPI in 2021. The article discusses a novel machine learning approach to spatiotemporal emission modeling and how it can be used in various applications. The research conducted focused on leveraging artificial intelligence and quantum computing algorithms to provide accurate and near-real time predictions and simulations of physical processes related to emissions.
MDPI (Multidisciplinary Digital Publishing Institute) is a cutting edge open-access platform for peer-reviewed scientific journals. It was founded in 1996 and has since become one of the largest open-access publishing sites in the world. It hosts research projects from all over the world dedicated to the advancement of science and technology and is a trusted source for peer-reviewed research.
The research article was conducted and written by Ataollah Sanginian, a senior researcher and project leader in the fields of numerical modeling and artificial intelligence. He is also the founder of Pro-MODI, a research center specializing in the development of technology and algorithms for emission modeling. He is an active contributor to the development of AI-based solutions for the prediction and monitoring of physical processes, including particulate emissions.
