A Missouri State University team claimed first place in the prestigious 2025 Electric Power Outreach Institute (EPRI) Fusion Quantum Challenge.

The team comprised Dr. Ridwan Sakidja, professor of physics, astronomy and materials science, and Gaige Riggs, a Master of Science in Materials Science graduate. They took home the first-place prize.

About the challenge

This year’s global competition brought together 23 submissions from 12 countries to develop quantum-driven solutions in two critical areas: radiation-resistant materials and plasma stability.

Participants had to use quantum computing to simulate how fusion materials degrade under intense radiation, an essential hurdle in building sustainable energy systems. Sakidja and Riggs tackled this challenge by developing an innovative approach that simplifies the quantum modeling process. This makes it feasible with today’s limited and noisy quantum hardware.

“Our proposal uses quantum computing to model radiation damage by shrinking and compressing the Hamiltonian, so it becomes manageable for quantum hardware,” Sakidja said. “We’re thrilled this approach was recognized and excited about its potential for future materials research.”

The Hamiltonian is a mathematical representation of the total energy of a system, including both kinetic and potential energy. In quantum mechanics, it plays a central role in predicting how a system evolves over time, making it essential for modeling complex physical phenomena like radiation damage.

A powerful concept

The team’s winning submission focused on reducing the complexity of radiation damage modeling. Fusion energy systems subject materials to intense stress and bombardment from neutrons. However, today’s quantum devices struggle with the large-scale simulations such systems require.

To solve this, Sakidja and Riggs used recently developed sparsification and compression techniques to “shrink” the quantum problem to its essential elements. Their model zeroed in on the most affected regions of the material, then applied quantum algorithms optimized for the Noisy Intermediate-Scale Quantum era.

“I believe the jury appreciated our main philosophy to scale up,” Sakidja said. “By making the model smaller and more focused, we could design a solution that fits within the realistic capabilities of today’s quantum systems.”

Lessons and next steps

As part of its prize, the team will co-author a white paper with fellow winners to share their techniques with the scientific community.

According to Sakidja, the biggest challenge was distilling a complex, dynamic physical problem into a form suitable for current quantum machines.

“This is only the beginning,” he said. “We’re excited about quantum computing’s rapid growth and proud to be preparing our students for a future where these skills will be in high demand across industries.”

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