Hybrid Quantum-Classical Processing Workflows in Modular Supercomputing Architectures for Data-Intensive Earth Observation Applications

The implementation of scalable and efficient Earth Observation (EO) processing workflows is essential to improve the access to and analysis of the vast amount of multi-source Remote Sensing (RS) data and to provide decision-makers with clear, timely, and useful information. This project aims at developing hybrid quantum-classical processing workflows with a Modular Supercomputing Architecture (MSA) system that integrates heterogeneous High-Performance Computing (HPC) systems, including different types of accelerators (GPUs, FPGAs) and next-generation computing technologies (quantum and neuromorphic computing). This research will be among the pioneers to use the Jülich UNified Infrastructure for Quantum computing (JUNIQ), which integrates quantum computers and quantum annealers in the form of quantum-classical hybrid computing systems into the MSA environment of the Jülich Supercomputing Centre (JSC). This project will build upon an existing EO processing workflow that can automatically generate high spatial land cover maps at national and continental scale in a completely unsupervised procedure. The project will propose an MSA-based EO processing workflow that will include hybrid quantum-classical algorithms optimized for the processing of large amounts of RS data. The workflow will exploit the flexibility of the MSA system by selecting the right mix of computing resources and assigning each processing task to be run on an exactly matching computing platform.

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