2023/11/21 by Weiwen Jiang, Jiang, Weiwen, Youzuo Lin +1
Computer Science · Earth and Planetary Sciences · Engineering · #FOS: Physical sciences #Geophysical Methods and Applications #Quantum Physics (quant-ph) #Seismic Imaging and Inversion Techniques #Seismology and Earthquake Studies
paper · pdf · doi:10.48550/arxiv.2311.12333
openalex publication_date 2023/11/21 · openalex created_date 2023/11/23 · openalex updated_date 2026/07/28
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.