Quantum for Energy and Utilities 2026

Energy generation and supply includes the extraction, production and processing of primary energy sources. This sector is characterized by intensive capital expenditure, global logistics networks and molecular-level engineering challenges that push classical simulation to its limits. Energy (generation and supply)2 From seismic imaging and methane sensing to wind optimization and smart charging, quantum is advancing energy generation and supply. 2.1 Fossil fuels The oil and gas industry has long relied on HPC to address computationally intensive challenges, from upstream seismic data inversion to downstream molecular design. However, as the complexity of these problems grows, the industry is exploring new computational paradigms. Quantum technologies are a potential source of competitive advantage across the entire hydrocarbon value chain. Upstream (exploration and production) The upstream segment remains the most capital- intensive phase of the hydrocarbon value chain. It has to balance two competing priorities: extracting as much value as possible from existing oil fields while keeping the environmental impact of exploration as low as possible. The biggest computational constraints are in subsurface imaging and reservoir simulation. Subsurface imaging and seismic inversion Seismic imaging requires processing petabytes of data to rebuild detailed pictures of underground geology. The central maths problem is an inverse one: using recorded seismic waves to infer the subsurface properties that produced them, which means effectively working backwards from the wave equation. Seismic data-processing techniques such as traditional full waveform inversion (FWI) often struggle because the optimization landscape is highly non-convex, so it can get stuck in local minima and produce misleading subsurface models. Quantum computing approaches such as quantum annealing are expected to yield improved solutions over classical computing approaches.Quantum sensing for gravimetry Beyond advances in computing, quantum sensing is starting to reshape exploration workflows, allowing for precise, non-invasive imaging of oil and gas reservoirs. Quantum gravimeters that use cold-atom interferometry can measure absolute gravity with exceptionally high stability and essentially no drift. That is a major advantage over traditional spring- based gravimeters, which tend to drift mechanically and need frequent recalibration. By leveraging the wave-like behaviour of atoms, these quantum sensors can estimate gravitational acceleration with extremely high precision. Reservoir simulation Modelling fluid flow in porous rock means solving non-linear partial differential equations (PDEs) on grids with millions of cells. Most of the runtime comes from the large linear systems that have to be solved at every time step, and the cost grows mainly with the size of those systems. Quantum solutions for solving difficult PDEs are being developed in anticipation of hardware availability. Midstream (transportation and pipelines) Midstream operations are essentially about logistics and network optimization. Moving hydrocarbons requires coordinating pipelines, ships and terminals through intricate schedules, and these challenges are often framed as NP- hard combinatorial optimization problems. Hybrid quantum-classical solvers (for example variational approaches such as QAOA or quantum Quantum technologies are a potential source of competitive advantage across the entire hydrocarbon value chain. Quantum for Energy and Utilities: Key Opportunities for Energy Transition 11