Cloud computing now underlies most industrial and academic work, making it well-suited for processing big data close to its source. Combined with HPC, cloud infrastructure, enhanced by GPUs, containerization, and microservices, they increasingly supports AI/ML workloads once limited to HPC environments. Quantum computing is also emerging as a complementary tool, with hybrid quantum-classical methods showing promise for optimisation, simulation, and machine learning in Earth system science.
Session Focus
This session focuses on use cases involving the entire computing spectrum, from Cloud to HPC and to emerging Quantum computing, to o assess current progress and identify steps for wider adoption of these resources in Earth Observation and Earth Modeling. Contributions are welcomed across the following areas:
• Big Data Infrastructures and Platforms: Case studies, techniques, models, and algorithms for data processing on the cloud and HPC.
• Cloud Federations, Interoperability, and Sustainability: Scalability and interoperability across domains, multi-provenance data management, security, privacy, and green/sustainable computing practices.
• Cloud-Native and HPC AI/ML Frameworks: Tools and frameworks for processing data using AI and ML on the cloud and/or HPC, including operational deployment and data lake/warehouse management.
• Convergence of Cloud Computing and HPC: Workload unification and hybrid architectures for Earth Observation (EO) data processing across cloud and HPC environments.
• Quantum Computing Algorithms for Earth Science: Quantum and quantum-inspired algorithms for optimisation, simulation, and data analysis in EO and Earth system modeling.
• Hybrid Quantum-Classical and Quantum-HPC Architectures: Integration of quantum processing units (QPUs) with classical HPC and cloud infrastructures, including workflow orchestration and resource scheduling.
• Quantum Machine Learning: Quantum-enhanced ML techniques for remote sensing, climate, and geospatial data processing.
• Benchmarking, Access, and Readiness for Quantum Resources: Evaluating quantum hardware/simulators, cloud-based quantum access models, and assessing technology readiness for operational Earth science applications.

We encourage researchers, practitioners, and industry experts to share their insights, case studies, and innovative solutions that promote the integration of cloud computing, HPC, and quantum computing in Earth Observation and Earth Modeling.