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DTSTAMP:20240626T180034Z
LOCATION:3004\, 3rd Floor
DTSTART;TZID=America/Los_Angeles:20240626T103000
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UID:dac_DAC 2024_sess132_RESEARCH562@linklings.com
SUMMARY:QuGeo: An End-to-end Quantum Learning Framework for Geoscience ---
  A Case Study on Full-Waveform Inversion
DESCRIPTION:Research Manuscript\n\nWeiwen Jiang (George Mason University) 
 and Youzuo Lin (University of North Carolina, Chapel Hill)\n\nThe rapid ad
 vancement of quantum computing has generated considerable anticipation for
  its transformative potential. However, harnessing its full potential reli
 es on identifying "killer applications". In this regard, QuGeo emerges as 
 a groundbreaking quantum learning framework, poised to become a key applic
 ation in geoscience, particularly for Full-Waveform Inversion (FWI). This 
 framework integrates variational quantum circuits with geoscience, represe
 nting a novel fusion of quantum computing and geophysical analysis. This s
 ynergy unlocks quantum computing's potential within geoscience. It address
 es the critical need for physics-guided data scaling, ensuring high-perfor
 mance geoscientific analyses aligned with core physical principles. Furthe
 rmore, QuGeo's introduction of a quantum circuit custom-designed for FWI h
 ighlights the critical importance of application-specific circuit design f
 or quantum computing. In the OpenFWI's FlatVelA dataset experiments, the v
 ariational 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 outpu
 t velocity map. This is a notable enhancement from the baseline design's S
 SIM score of 0.800, which was achieved without the incorporation of physic
 s knowledge.\n\nTopic: Design\n\nKeyword: Quantum Computing\n\nSession Cha
 ir: Kanad Basu (The University of Texas)
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