en.Wedoany.com Reported - US-based Quantinuum and UK-based bp have launched a new quantum computing project aimed at developing quantum algorithms for subsurface seismic imaging. Quantinuum announced on May 20 that the two parties will expand their collaboration beyond a previous feasibility pilot to use quantum computing methods for simulating more complex subsurface wave physics, serving the energy industry's needs for imaging Earth's subsurface structures and locating resources.
Seismic imaging is an extremely computationally intensive part of oil and gas exploration. Its core task is to convert data generated by seismic waves propagating, reflecting, and refracting underground into interpretable geological structure images. According to bp's official website, seismic imaging is one of its core capabilities, supporting global exploration decisions by sending sound waves below the seabed and analyzing the reflected results. bp has also long utilized high-performance computing for subsurface modeling; its HPC center resources have been used to process geophysical data and seismic images, helping scientists determine subsurface structures. With quantum algorithms entering this workflow, the research focus is not on replacing existing geological interpretation expertise, but on exploring whether quantum processing methods can reduce the computational pressure brought by complex wave equations and high-resolution imaging.
Quantinuum stated in the announcement that as traditional computers pursue higher spatial resolution in seismic imaging, demands on computational resources like memory increase accordingly. The company's explanation is that, ideally, a quantum computer can double the dimensionality of the quantum state space by adding a single qubit, thus offering a theoretical path different from classical computing for the same resolution improvement. This statement describes the theoretical capabilities of quantum computing and does not equate to the current project having already achieved an industrial-grade replacement.
This project adopts a hybrid quantum-classical technical approach. Quantinuum stated that the hybrid quantum-classical method allows the quantum processor to handle the most difficult computational parts, while the classical system continues to manage data logic, keeping the results within real physical constraints. This is particularly critical for subsurface imaging tasks: oil and gas exploration does not deal with abstract mathematical problems, but with complex physical systems influenced by strata, salt domes, faults, pore structures, wave velocity variations, and acquisition conditions. For algorithms to enter engineering workflows, they must preserve the verifiability of the geophysical model beyond just computational efficiency.
Quantinuum CEO Rajeeb Hazra stated that this collaboration could become an important industrial use case for quantum computing, offering a more efficient path for energy exploration by obtaining higher fidelity data at a lower computational cost than classical computing. The company also emphasized that the project continues the success of the previous feasibility pilot and is now transitioning to simulating more complex subsurface properties. bp's participation in this project also indicates that energy companies are integrating quantum computing into their long-term toolbox for high-performance computing and geophysical modeling, moving beyond the proof-of-concept stage.
The collaboration between US-based Quantinuum and UK-based bp extends the application of quantum computing from common areas like materials, chemistry, and finance further into seismic imaging. For oil and gas exploration and subsurface resource management, imaging accuracy, computational cycle time, and energy consumption costs directly impact exploration efficiency and decision quality. If quantum hybrid algorithms can continue to advance within real geophysical models, seismic imaging is poised to become a key testing ground for quantum computing's entry into physical industrial processes.
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