Industry · Early Pilots
Quantum Computing for Oil, Gas, and Mining
Oil, gas, and mining companies handle huge simulation and optimization workloads and some of the world's largest datasets. Quantum computing and quantum sensing could affect exploration, reservoir modeling, and process chemistry, and energy majors have been among the earliest corporate members of quantum networks.
Maturity
Early pilots in optimization and chemistry; quantum sensing is the most mature part
Most credible computing workload
Chemistry for processing and carbon storage, then selected linear algebra and optimization
Most mature quantum technology
Gravimeters and magnetometers for exploration
Strongest classical competition
Exascale HPC, GPU seismic processing, and machine learning
Notable moves
ExxonMobil in the IBM Quantum Network, Aramco with Pasqal, TotalEnergies with Quantinuum
Realistic horizon
Sensing in the late 2020s; computing advantages in the 2030s at the earliest
Overview
Extractive industries find and produce resources buried kilometers underground, and nearly every step produces a computational problem. Seismic surveys generate petabytes of data that must be processed into images of the subsurface. Reservoir simulators model the flow of oil, gas, and water through porous rock to plan wells. Mining companies model ore bodies, schedule fleets, and optimize processing plants. These industries have long been users of the largest supercomputers, and their interest in quantum technology follows from that habit.
Energy majors were early corporate adopters. ExxonMobil joined the IBM Quantum Network in 2019, TotalEnergies has worked with Quantinuum, and Saudi Aramco installed a Pasqal neutral atom machine. The activity includes quantum computing, but also quantum sensing, where gravimeters and magnetometers may improve exploration. Both areas are at an early stage, and the most mature items are sensors.
Why this industry is a candidate for quantum computing
Subsurface imaging and reservoir modeling reduce to solving large partial differential equations and linear systems, which are at least conceptually matched to quantum linear algebra. However, the data sets are enormous, loading them into quantum states is a major obstacle, and classical exascale methods and machine learning are advancing quickly.
Quantum sensing is a more direct match. A gravimeter that measures tiny differences in gravity can detect density contrasts underground, such as caves, ore bodies, or aquifers, and quantum magnetometers can map magnetic anomalies. Because these instruments are already commercially available in early forms, they may reach field use before quantum computers help with simulation.
Key facts and numbers
| Item | Detail |
|---|---|
| Aramco and Pasqal | A 200-qubit neutral atom system installed in Aramco's data center |
| ExxonMobil | First energy company to join the IBM Quantum Network, in 2019 |
| Seismic data scale | Modern surveys generate petabytes of raw data |
| Reservoir models | Grids with millions to hundreds of millions of cells, run on clusters |
| Quantum gravimeters | Cold-atom instruments are commercially available for geophysical surveys |
| Carbon storage | Accurate chemistry for sorbents and mineral reactions is a stated long-term target |
Where quantum computing could help
Seismic imaging and reservoir simulation
The problem: Turning seismic recordings into images requires solving wave equations over huge volumes, and reservoir simulation solves multiphase flow equations over millions of cells for many scenarios.
The quantum approach: Quantum linear solvers, Hamiltonian simulation, and quantum algorithms for differential equations could speed up parts of these calculations in principle.
Where things stand: Theory shows possible speedups under assumptions about data access and conditioning, which are hard to meet for subsurface data. Companies run proof-of-concept studies with partners, while exascale GPU computing and machine learning deliver practical gains today.
Related algorithms: HHL Algorithm, Quantum Singular Value Transformation (QSVT), Variational Quantum Linear Solver (VQLS)
Quantum sensing for exploration
The problem: Finding ore bodies, cavities, and oil-bearing structures from the surface needs sensors that detect small gravity and magnetic variations.
The quantum approach: Atom-interferometer gravimeters and gradiometers, and quantum magnetometers, offer high sensitivity and low drift, improving survey quality and speed.
Where things stand: Commercial cold-atom gravimeters exist and have been field tested for geophysics. Cost, ruggedness, and survey speed are the main limits, and mining and energy firms are among the early customers.
Related algorithms: Quantum Error Mitigation Techniques
Mine planning, logistics, and plant optimization
The problem: Open-pit mine sequencing, fleet dispatch, and processing plant scheduling involve large combinatorial optimization with uncertain ore grades.
The quantum approach: These are encoded as optimization problems for annealers, QAOA, and quantum-inspired solvers, and the stochastic parts could use amplitude estimation.
Where things stand: Pilots address small instances, and classical mixed-integer and simulation tools are strong. ExxonMobil's published study on optimizing maritime shipping routes with IBM illustrates the type of problem, with modest results.
Related algorithms: Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, Quantum Amplitude Estimation
Process chemistry, catalysts, and carbon storage
The problem: Refining, leaching, flotation, and carbon capture and storage rely on catalysts and reagents whose behavior is hard to predict, and storing carbon dioxide underground involves mineral reactions and sorbent chemistry.
The quantum approach: Quantum simulation of catalysts and sorbents with phase estimation, and hybrid methods for smaller systems.
Where things stand: TotalEnergies has collaborated with Quantinuum on carbon capture materials, and other majors fund chemistry research. As elsewhere, results concern small molecules, and the problems of real value need fault-tolerant machines.
Related algorithms: Quantum Phase Estimation (QPE), Variational Quantum Eigensolver (VQE)
Who is working on it
- ExxonMobil, with IBM, on optimization and materials research, as an early member of the IBM Quantum Network.
- Saudi Aramco, with Pasqal, which deployed a neutral atom quantum computer and explores simulation and optimization.
- TotalEnergies, with Quantinuum and others, on chemistry for carbon capture and on optimization.
- Mining and geophysics firms and universities that test quantum gravimeters and magnetometers for exploration.
- Quantum sensing and hardware vendors including Q-CTRL, Infleqtion, IBM, and Pasqal.
Realistic timeline
| Horizon | What to expect |
|---|---|
| Now to 2028 | Quantum sensors are field tested for exploration. Computing pilots in optimization and chemistry continue, with classical HPC delivering most gains. |
| 2028 to 2032 | Early fault-tolerant machines enable small chemistry and linear algebra demonstrations. Sensors enter routine use in niche surveys. |
| 2032 and later | If scalable hardware arrives, chemistry for processing and storage could benefit. Seismic and reservoir simulation advantage remains uncertain. |
How to judge quantum claims in this sector
Extractive firms have a long record of evaluating new computing technologies, from vector supercomputers to GPUs, by running representative workloads and measuring cost per result. The same discipline is the best way to evaluate quantum claims: take a real seismic or reservoir benchmark, define a target accuracy, and compare total cost and time, including data movement, against the best GPU implementation.
Sensor claims can be evaluated with field data. A quantum gravimeter should be judged by its noise floor, drift, and survey time against a conventional instrument in the same location, and by whether it reveals features that conventional surveys missed. Those are concrete tests, and they are more informative than laboratory sensitivity numbers.
What stands in the way
- Data scale: seismic and reservoir data are too large to load into quantum states with current methods.
- Classical progress: GPU and machine learning methods are improving imaging and simulation rapidly.
- Ruggedness and cost: field sensors must work in harsh environments and justify their price.
- Business cycles: capital budgets for research follow commodity prices, which can interrupt long-term programs.
What organizations in this sector should do now
- Pilot quantum gravimetry and magnetometry in exploration programs where conventional surveys struggle.
- Define chemistry problems in processing and carbon storage where better accuracy would change decisions.
- Participate in consortia and national programs to share cost, and benchmark any computing pilot against GPU baselines.
- Maintain a small expert team to follow resource estimates and hardware progress.
Myths versus reality
- Myth: quantum computers will find oil. Reality: sensors and simulation might help, and exploration depends on geology, data, and drilling.
- Myth: seismic processing will move to quantum computers soon. Reality: data loading and classical GPU progress make that unlikely in the near term.
- Myth: quantum sensing is far off. Reality: commercial gravimeters already exist.
The bottom line
For oil, gas, and mining, quantum sensing is the nearest-term opportunity, and chemistry for processing and storage is the strongest computing case. Seismic and reservoir simulation are long shots. Firms should test sensors in the field and keep computing efforts modest and benchmarked.
Frequently asked questions
Is quantum computing used in oil and gas?
Companies run pilots and partnerships, such as ExxonMobil with IBM and Aramco with Pasqal, but production workflows use classical high-performance computing.
What are quantum gravimeters?
Instruments that use cold atoms to measure tiny changes in gravity, helping to detect underground density contrasts for exploration and surveying.
Could quantum computers speed up seismic imaging?
In theory for some linear algebra steps, but loading petabytes of data and competing GPU methods make a near-term advantage unlikely.
How could quantum computing help carbon capture and storage?
By simulating sorbents, catalysts, and mineral reactions more accurately, which is a long-term chemistry application.
What should extractive firms do now?
Test quantum sensors in the field, define chemistry problems for future simulation, and benchmark any computing pilot against GPU baselines.
Keep exploring
- Algorithm: HHL Algorithm
- Algorithm: Quantum Singular Value Transformation (QSVT)
- Algorithm: Quantum Phase Estimation (QPE)
- Algorithm: Quantum Approximate Optimization Algorithm (QAOA)
- Hardware: Pasqal Fresnel (Orion generation)
- Hardware: IBM Heron (r1, r2, and r3)
- Company: Pasqal
- Company: Quantinuum
- Company: Q-CTRL
- Research: Higher-Accuracy Molecular Energy Estimation Using Adaptive VQE Ansätze
- Industry: Quantum Computing for Energy and Utilities
- Industry: Quantum Computing for Climate and the Environment
- Industry: Quantum Computing for the Chemical Industry
- Industry: Quantum Computing for Logistics
This page is written for educational purposes and summarizes public announcements, published research, and published resource estimates (last reviewed September 30, 2026). Vendor and customer claims have not all been independently verified, timelines are estimates, and nothing here is investment or business advice.