  The rapid advancement of quantum computing has generated considerable
anticipation for its transformative potential. However, harnessing its full
potential relies on identifying "killer applications". In this regard, QuGeo
emerges as a groundbreaking quantum learning framework, poised to become a key
application in geoscience, particularly for Full-Waveform Inversion (FWI). This
framework integrates variational quantum circuits with geoscience, representing
a novel fusion of quantum computing and geophysical analysis. This synergy
unlocks quantum computing's potential within geoscience. It addresses the
critical need for physics-guided data scaling, ensuring high-performance
geoscientific analyses aligned with core physical principles. Furthermore,
QuGeo's introduction of a quantum circuit custom-designed for FWI highlights
the critical importance of application-specific circuit design for quantum
computing. In the OpenFWI's FlatVelA dataset experiments, the variational
quantum circuit from QuGeo, with only 576 parameters, achieved significant
improvement in performance. It reached a Structural Similarity Image Metric
(SSIM) score of 0.905 between the ground truth and the output velocity map.
This is a notable enhancement from the baseline design's SSIM score of 0.800,
which was achieved without the incorporation of physics knowledge.
