In an era of customized hardware designed to tackle specific geophysical challenges, recent advancements in quantum computing could offer promising solutions to complex global optimization problems using hybrid quantum-classical solvers. We introduce an application of seismic data processing where the stack power of the traces is maximized through quantum annealing for surface-consistent phase and amplitude refraction residual calculations. Refraction residual corrections are a necessary step in the processing workflow to obtain a detailed characterization of the subsurface. The problem of estimating accurate refraction residuals can be solved in a framework where relative time shifts and amplitude distortions of seismic traces are evaluated in multiple common-midpoint-offset bins through computerized cross-correlation operations. The results are then used to build a system of linear equations that is simultaneously inverted for surface-consistent shot and receiver phase and amplitude variations. We rewrote the problem by substituting the cross-correlation operations with global optimization performed by a “black-box” hybrid solver, which uses a physical quantum-annealing machine. With the projected hardware and technology improvements of the near future, the new approach is expected to be more accurate than the current solution because it prevents the phenomenon of the so-called cycle skips in the cross-correlation process. The method has been benchmarked with a classical global optimization algorithm and tested with promising results on synthetics of industrial size and relevance. A first attempt of application on field data has also been carried out. Quantum-annealing-based global optimization can be applied to other disciplines such as material science, logistics, and finance.

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