Industry · Exploratory
Quantum Computing for Climate and the Environment
Quantum simulation of molecular systems could accelerate discovery of next-generation battery materials, more efficient solar cells, and catalysts for carbon capture — among the most credible medium-term quantum computing applications.
Maturity
Exploratory; chemistry for climate technologies is the strongest case
Most credible quantum workload
Materials for carbon capture, storage, and clean fuels
Weakest case
Speeding up global climate models directly
Hardware needed
Fault-tolerant machines with thousands of logical qubits for chemistry
Most mature quantum technology
Sensing, such as gravimeters and magnetometers for environmental monitoring
Realistic horizon
Sensing now to 2030; chemistry impact in the 2030s at the earliest
Overview
Climate and environmental science depends on computation at enormous scale. Climate models divide the atmosphere and oceans into millions of grid cells and step them forward in time, consuming some of the largest supercomputers in the world, and the chemistry of carbon capture, clean fuels, and pollution control rests on molecular behavior that is hard to compute accurately. Quantum computing is sometimes presented as a way to solve climate change, which overstates the case, but it could contribute in a few specific ways.
The most credible contributions are indirect: better materials for capturing carbon, storing energy, and making clean fuels, through more accurate chemistry; and quantum sensors that measure gravity, magnetic fields, and time with new precision for monitoring ice, water, and the Earth's interior. The proposals to speed up the climate models themselves are theoretical and face serious obstacles.
Why this industry is a candidate for quantum computing
Climate models are built on nonlinear partial differential equations for fluid flow, radiation, and chemistry. Their cost is dominated by resolution and by the need to run many scenarios. Quantum algorithms for differential equations exist, but they work best for linear problems and dissipative systems, and the chaotic, nonlinear nature of the atmosphere is a poor match.
The chemistry side is much better aligned. Designing a sorbent that captures carbon dioxide from air, a catalyst that turns it into fuel, or a better battery involves the electronic structure of molecules and materials. Simulating these accurately is the best-established use of fault-tolerant quantum computers, and climate-related research programs fund it for that reason.
Key facts and numbers
| Item | Detail |
|---|---|
| Atmospheric CO2 | About 420 parts per million, which drives interest in capture and conversion chemistry |
| NASA Cold Atom Laboratory | On the International Space Station since 2018, producing ultracold atoms in orbit |
| Climate model resolution | Global models typically use grid cells of tens to about a hundred kilometers, with kilometer-scale runs on exascale systems |
| Quantum differential equation solvers | Speedups proven mainly for linear or strongly dissipative cases |
| Carbon capture materials | Metal-organic frameworks have hundreds of thousands of possible structures, and binding energies require accurate chemistry |
| Quantum gravimeters | Commercially available cold-atom instruments for geophysical surveying |
Where quantum computing could help
Carbon capture and conversion materials
The problem: Capturing carbon dioxide from flue gas or air requires materials, such as metal-organic frameworks and amine sorbents, that bind it strongly enough to capture and weakly enough to release with little energy. Screening thousands of candidates with accurate chemistry is expensive.
The quantum approach: Quantum simulation could compute binding energies and reaction barriers for promising candidates more accurately than approximate classical methods, especially when metal centers are involved.
Where things stand: TotalEnergies has worked with Quantinuum on quantum chemistry for carbon capture materials, and other groups run small studies. Calculations on today's devices cover fragments, not whole materials. Classical simulation and machine learning currently do most of the screening.
Related algorithms: Quantum Phase Estimation (QPE), Variational Quantum Eigensolver (VQE)
Climate and weather modeling
The problem: Weather and climate models solve nonlinear fluid equations on global grids at ever finer resolution, and ensembles of runs quantify uncertainty. Costs are high, and resolution is limited.
The quantum approach: Researchers have proposed quantum algorithms for fluid equations based on Hamiltonian simulation, linear solvers, and quantum Carleman linearization for nonlinear equations.
Where things stand: Results show quantum speedups only under conditions, such as weak nonlinearity or strong dissipation, that real atmospheric flows do not meet, and state loading and readout add heavy costs. Operational centers are instead adopting machine learning forecasts and exascale computing. Quantum climate modeling is a research topic.
Related algorithms: HHL Algorithm, Quantum Singular Value Transformation (QSVT), Trotter-Suzuki Decomposition
Environmental sensing and monitoring
The problem: Measuring changes in ice sheets, groundwater, sea level, and subsurface structures, and detecting leaks of methane or other gases, needs sensors with high sensitivity and stability.
The quantum approach: Atom-interferometer gravimeters, quantum magnetometers, and optical clocks measure tiny changes in gravity, magnetic fields, and time, and can map mass changes underground and in ice.
Where things stand: Quantum gravimeters and magnetometers are available commercially for geophysics and surveying. Space-based cold-atom experiments, including NASA's Cold Atom Laboratory on the International Space Station, test technology for future Earth observation. Costs and portability are the main barriers to wide use.
Related algorithms: Quantum Error Mitigation Techniques
Risk analysis and resource planning
The problem: Insurers, governments, and utilities simulate climate risk, flooding, and supply disruption with Monte Carlo methods across many scenarios.
The quantum approach: Quantum amplitude estimation offers a quadratic speedup for estimating expected losses, in the same way as for financial risk.
Where things stand: The speedup is real in theory, but it requires large fault-tolerant machines and does not address the larger uncertainty, which comes from model assumptions and data.
Related algorithms: Quantum Amplitude Estimation, Quantum Monte Carlo Integration
Who is working on it
- TotalEnergies and Quantinuum, on quantum chemistry for carbon capture materials.
- National laboratories and universities that study quantum algorithms for fluid dynamics and plasma and combine them with classical climate science.
- Space agencies, including NASA, which operate cold-atom experiments in orbit to prepare quantum sensors for Earth observation.
- Geophysics and surveying companies that use quantum gravimeters and magnetometers, and sensing startups such as Q-CTRL and Infleqtion.
- Chemical and materials companies that fund fundamental research on catalysts and sorbents with hardware and software partners.
Realistic timeline
| Horizon | What to expect |
|---|---|
| Now to 2030 | Quantum sensors are used in niche geophysics and surveying. Chemistry pilots on small molecules run on available hardware, with classical AI doing most of the screening. |
| 2030 to 2035 | Early fault-tolerant machines may allow improved calculations for catalyst and sorbent fragments. Space-based quantum sensors begin flight tests. |
| 2035 and later | If hardware scales, quantum-enhanced design of carbon capture and clean fuel materials could contribute. Direct climate modeling impact remains doubtful. |
How to judge quantum claims in this sector
Claims that quantum computing will transform climate modeling should be treated with caution. The central equations are nonlinear and chaotic, the dominant uncertainty comes from physics and data rather than compute, and the best current progress comes from machine learning emulators and exascale simulations. A credible claim would state which part of a model it accelerates and show a classical comparison on the same problem.
Claims about materials and sensing are easier to evaluate. A carbon capture chemistry result should say which molecule was treated, with how many qubits, and how the result compares with accurate classical methods. A sensing claim should give sensitivity, stability, and field-test results in realistic conditions, which are concrete and checkable.
What stands in the way
- Nonlinearity: the equations of the atmosphere and oceans are nonlinear and chaotic, which limits quantum speedups.
- Time: climate mitigation is urgent, while fault-tolerant quantum computers are at least several years away.
- Competing tools: machine learning and exascale systems are advancing rapidly for modeling and materials screening.
- Translation: a computed material must still be synthesized, tested, and scaled.
What organizations in this sector should do now
- Prioritize proven climate technologies now, and treat quantum computing as a longer-term research bet, not a substitute for action.
- Use classical AI and HPC for materials screening today, and define the accuracy gaps that quantum methods might fill.
- Consider quantum sensors for monitoring where they are commercially available.
- Support open benchmarks that compare quantum and classical chemistry on climate-relevant molecules.
Myths versus reality
- Myth: quantum computers will solve climate change. Reality: they might help design materials, but emissions cuts depend on deployment of existing technology.
- Myth: quantum computing will make climate models exact. Reality: uncertainty in climate projections is dominated by physics and data, not by raw compute.
- Myth: all quantum climate tools are far off. Reality: quantum sensors are already used in surveying and geophysics.
The bottom line
Quantum technology can support climate goals through materials and measurement, but it is not a substitute for deploying known solutions today. A realistic plan funds quantum research as a long-term complement while delivering emissions cuts with existing technology.
Frequently asked questions
Can quantum computers help fight climate change?
Possibly, mainly through chemistry for carbon capture, batteries, and clean fuels. They are not a substitute for deploying known solutions now.
Will quantum computers improve climate models?
Not in the near term. Quantum algorithms for differential equations are limited by nonlinearity, and data loading is costly.
What are quantum gravimeters?
Instruments that use cold atoms to measure small changes in gravity, which can reveal changes in groundwater, ice, and subsurface structure.
Which companies work on carbon capture chemistry with quantum computers?
TotalEnergies has collaborated with Quantinuum, and other energy and chemical companies run similar studies.
What is the NASA Cold Atom Laboratory?
An experiment on the International Space Station that produces ultracold atoms in microgravity, used to test quantum sensors that might later measure Earth's gravity from orbit.
Keep exploring
- Algorithm: Quantum Phase Estimation (QPE)
- Algorithm: Variational Quantum Eigensolver (VQE)
- Algorithm: HHL Algorithm
- Algorithm: Quantum Amplitude Estimation
- Hardware: Quantinuum H2
- Company: Quantinuum
- Company: Q-CTRL
- Research: Higher-Accuracy Molecular Energy Estimation Using Adaptive VQE Ansätze
- Research: Automated Active Space Selection Improves Practical VQE Chemistry Simulations
- Industry: Quantum Computing for Energy and Utilities
- Industry: Quantum Computing for Oil, Gas, and Mining
- Industry: Quantum Computing for the Chemical Industry
- Industry: Quantum Computing for Materials Science
- Industry: Quantum Computing for Agriculture and Food
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.