Can Quantum Computing Help Solve Our Toughest Energy Problems? With UT Austin geophysicist Mrinal Sen

Quantum computing could eventually help scientists model the subsurface, improve carbon storage and tackle complex energy challenges.

As artificial intelligence drives unprecedented interest in computing and unprecedented demand for electricity, another emerging technology could eventually change not only how much computing power is available, but the kinds of problems computers can solve.

Quantum computing remains years away from widespread commercial deployment. But Dr. Mrinal Sen, a professor of geophysics at the University of Texas at Austin, Jackson School of Geosciences, believes it could ultimately become a powerful tool for tackling some of the energy industry’s most computationally demanding challenges.

Speaking with Path to Zero host Tucker Perkins, Sen explained how quantum computing could complement conventional computing and artificial intelligence in areas ranging from subsurface imaging and carbon storage to energy optimization and digital twins.

What Makes Quantum Computing Different?
Traditional computers process information using bits represented as zeros and ones. Quantum computers use quantum bits, or qubits, which can exist in combinations of states through a phenomenon known as superposition.

Sen compared it to flipping a coin. Once the coin lands, it is heads or tails. While it is spinning in the air, however, it isn’t simply one or the other.

Combined with another quantum phenomenon known as entanglement, that characteristic could allow quantum computers to process certain kinds of complex problems very differently from conventional machines.

For the energy industry, the potential advantage isn’t simply having a faster computer. It is being able to tackle simulations and optimization problems that can become extraordinarily difficult as their complexity increases.

Seeing What’s Happening Underground

One example is carbon storage. When carbon dioxide is injected underground, scientists need to understand how the CO2 plume moves through the subsurface and whether it is remaining safely contained. Creating detailed three-dimensional images of what is happening underground can require enormous amounts of computing power.

Sen believes quantum computing could eventually make those calculations substantially faster.

The same fundamental challenge exists across geoscience. Scientists collect seismic data to develop images of structures beneath the Earth’s surface, but Sen said producing a useful image can currently take anywhere from six months to a year.

His ultimate goal is dramatically more ambitious: real-time subsurface imaging.

Sen compared the idea to an ultrasound, where a technician can see an image as data is being collected and adjust the equipment to focus on an area of interest. Geoscientists can’t currently do the same thing underground. With enough computing capability, Sen envisions a future in which researchers could see subsurface images in the field and immediately adjust how they collect additional data.

Where Quantum Computing Could Meet AI

Quantum computing and artificial intelligence are sometimes lumped together as emerging computing technologies. But Sen sees them playing different and potentially complementary roles.

AI and machine learning excel at finding relationships within large quantities of training data. Quantum computing could eventually help scientists generate complex simulations much faster, producing many more potential scenarios that can then be used to train machine-learning systems.

Those systems could help create more sophisticated digital twins—digital representations of physical systems that can be used to model conditions and make predictions.

In other words, quantum computing may not replace AI. In some applications, it could help give AI better information to work with.

Could Quantum Computing Actually Use Less Energy?

That question is especially relevant at a time when the enormous electricity requirements of AI and data centers have become a major energy issue.

Quantum computers themselves require significant amounts of energy. But Sen said the more important comparison is how much energy is required to complete a particular computational task.

If a problem that requires tremendous computing resources and time on conventional machines can eventually be solved much more quickly with a quantum computer, the total energy required for that task could potentially be lower.

That’s an important distinction. The promise isn’t necessarily an energy-efficient computer in the conventional sense. It’s the possibility of solving extremely difficult problems with fewer computational resources overall.

The Technology Still Has a Long Way to Go
Sen is also careful not to suggest that this transformation is imminent.

Today’s quantum computers remain vulnerable to noise and errors as the number of qubits increases. Sen said error-correcting quantum computers could potentially reach the market in roughly a decade, while fully error-free systems may be more than 15 years away.

That means much of the work happening today is about preparing for machines that don’t yet exist at the scale researchers ultimately need.
At UT Austin, Sen said his team is working to connect quantum computing with practical geoscience and energy challenges, developing workflows that can be “quantum ready” when the hardware catches up.

Preparing the People Along with the Technology
Sen believes another investment may be just as important as the hardware: people.

Solving a geoscience problem isn’t simply a matter of handing it to a computer scientist. Researchers need people who understand both the underlying science and how to translate those problems into algorithms that quantum computers can eventually process.

That means training the next generation of geoscientists in quantum mechanics and quantum computing while preserving the domain expertise needed to understand the physical problems themselves.

It’s a reminder that some of the biggest breakthroughs in energy may ultimately come not from a single technology, but from bringing different fields of expertise together.

Quantum computing isn’t ready to transform the energy industry today. But if researchers can eventually harness its capabilities, problems that now take months to solve could potentially be addressed dramatically faster. This could give scientists new ways to understand the Earth, manage carbon and optimize increasingly complex energy systems.

And that could turn a technology that still sounds like science fiction into a very practical energy tool.