Review Paper:
Quantum Computing
Applications for Geophysical Modeling of Earthquakes and Volcano Eruptions
Umaeswari P., Thenmozhi M., Vinod Kumar P., Santhosh K.N.S.K., Srinivasan J. and
Selvam Ponmurugan Panneer
Disaster Advances; Vol. 18(12); 99-104;
doi: https://doi.org/10.25303/1812da0990104; (2025)
Abstract
The growing complexity of geophysical systems, like earthquakes and volcanic eruptions,
requires computational models that can manage enormous, nonlinear and multidimensional
datasets in real time. Classical computing methods still yield results but are often
not designed to cope with the scales and stochasticity of seismic and volcanic observations,
so quantum computing provides a disruptive technology to tackle this issue, enabling
geophysical modeling to entirely transform into a capacity to process and analyze
complex patterns at massive scales. This study provides an overview of the potentials
of various quantum algorithms such as the Variational Quantum Eigensolver (VQE),
the Quantum Approximate Optimization Algorithms (QAOA) and quantum-enhanced Monte
Carlo simulations to simulate geophysical processes.
The results of these models will be of particular relevance to modeling partial
differential equations, inverse problems and tasks of uncertainty quantification
that describe seismic wave propagation, magma chamber flow and tectonic stress diffusion.
We will also discuss how quantum machine learning (QML) models can improve the forecasts
of earthquake epicenters, fault detections and eruption forecasts utilizing quantum
feature spaces. Further, we will include a discussion of both quantum sensors and
edge quantum processors, with attempts for in situ real-time data collection and
data processing in hazardous areas.
