Volcanic hazard understanding is rapidly evolving through advances in satellite Earth Observation, physics-based simulations, and Artificial Intelligence (AI). Meanwhile, emerging quantum technologies are opening new perspectives for dealing with the complexity of volcanic systems. This session explores this transition from classical to quantum technologies, emphasizing continuity as well as innovation in modeling, forecasting, and risk assessment.

We explicitly welcome contributions grounded in state-of-the-art classical approaches, including physics-based models, statistical and probabilistic frameworks, data assimilation, and Machine Learning (ML). Meanwhile, we encourage studies that investigate how quantum and quantum-inspired methods, including Quantum AI and Quantum ML, can extend, enhance, or complement these established techniques, particularly where high dimensionality, nonlinearity, and computational complexity represent fundamental bottlenecks.

Special attention is given to hybrid classical–quantum workflows, realistic near-term implementations, and comparative studies that critically assess the added value of quantum approaches with respect to existing methods, providing evidence-based benchmarks against well-established classical baselines.