The Potential of Quantum Computing for Geoscience

Divakar Vashisth, Tapan Mukerji, Mrinal K. Sen +3 more

Neetu Rani, Shweta Agarwal

Weiwen Jiang, Youzuo Lin

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.

M. Dukalski

Summary In the recent years quantum technologies have matured enough that modern quantum computers outperform their classical counterparts at some very special computational tasks. It is however not entirely clear what kind of practical problems can be solved using present or future quantum computers. Here we will try to give a first attempt at answering this question with focus on optimization problems in geoscience.

Milton Halem, Craig Pelissier, Michael M. Little +1 more

Divakar Vashisth, Rohan Sharma, Tapan Mukerji +1 more

The computational demands of geoscientific workflows are rapidly increasing in both complexity and scale, pushing the limits of classical computing. Quantum computing has emerged as a promising paradigm to address some of these challenges. This paper surveys the growing intersection between quantum computing and energy geosciences, highlighting recent developments and initiatives. To illustrate the practical relevance of this emerging field, we present a case study on seismic traveltime inversion using a variational quantum circuit. In this example, a hybrid quantum-classical workflow is used to reconstruct velocity models from noisy traveltime data, demonstrating how quantum circuits can solve geophysical inverse problems. While modest in scale, the study offers insights into the formulation and implementation of quantumassisted inversion. As quantum hardware advances and algorithmic frameworks mature, quantum computing is poised to become a transformative tool in the computational landscape of energy geosciences.