Speaker
Description
Urgent computing for disaster response currently faces severe computational bottlenecks. In the context of active volcanology, providing early warning capabilities and rapid impact forecasts for a wide spectrum of volcanic hazards—including those from explosive eruptions—requires complex numerical simulations and the real-time measurement, analysis, and assimilation of satellite and field data.
Currently, classical automated hazard assessment workflows demand extensive high-performance computing (HPC) resources that often fail to deliver adequate processing speeds during real-time emergencies. To address these challenges, this work investigates the integration of emerging quantum technologies into time-critical geoscience applications. Specifically, we propose a conceptual framework for a Hybrid Quantum-Classical Volcanic Hazard Assessment Workflow that leverages two distinct quantum advancements. First, we explore the incorporation of quantum-enhanced data acquisition, such as high-precision quantum sensors, to significantly improve the resolution and accuracy of initial observations. Second, we examine the application of quantum algorithms to data assimilation and probabilistic modeling. By doing so, we aim to reduce latency and enhance forecast reliability through superior optimization and sampling techniques. This poster outlines the theoretical architecture of the workflow, presents a roadmap for benchmarking performance, and seeks to foster interdisciplinary discussion to identify collaborative pathways for quantum algorithm development in the geosciences.