Speaker: Dr. Thilanka Munasinghe, Lead Research Specialist at the Rensselaer Institute for Data Exploration and Applications 

Abstract Title: Assessment of Quantum Maching Learning Applicability for Climate Actions

Abstract: Machine learning (ML) with data-driven solutions has been used recently to combat climate change-related problems; however, they face challenges stemming from traditional computational methods and lengthy training times, which impede their practical utility. Recent strides in quantum computing have permeated diverse domains, spanning from manufacturing engineering to other fields. With the potential to substantially reduce time and computational complexity, quantum computing shows promise in addressing climate change impacts. Its distinctive features make them well-suited for analyzing extensive climate datasets, simulating intricate climate models, optimizing resource allocation, and discerning patterns in climate data for mitigation and adaptation endeavors. This study explores the potential of using Quantum machine learning (QML) techniques on climate and weather data obtained from the NASA Giovanni tool. Two QML algorithms, the Quantum Support Vector Classifier (QSVC) and the Variational Quantum Classifier (VQC), have been used in combination with the IBM Qiskit ML 0.7.2 ecosystem (IBM 127-qubit Eagle). The methodology used so far, and the results, show that the QSVC and VQC, as QML algorithms, are capable of handling and predicting climate and weather data obtained from NASA satellites, which represents a breakthrough in the novel practical application of quantum computing.