The research, detailed in a paper published on arXiv, focuses on FLiBe—a molten salt composed of fluorine, lithium, and beryllium. In a fusion reactor, this material acts as a blanket that captures neutrons to produce tritium. Because tritium is exceptionally rare, the ability to optimize its extraction is a primary objective of the U.S. Department of Energy’s Genesis Mission. Classical computing methods have historically struggled to maintain the necessary accuracy when simulating these materials under the extreme heat and radiation found in a reactor.
To overcome these limitations, the research team employed a quantum-centric approach, integrating classical CPUs and GPUs with quantum processing units (QPUs). This hybrid framework allowed researchers to map the electronic structure of the salt and determine how it binds tritium at a fundamental molecular level. By offloading complex quantum circuits to a quantum computer while handling the broader simulation on classical hardware, the team extracted data regarding molecular stability and energetics that were previously inaccessible.
This project builds on earlier efforts to simulate large-scale biological systems, including proteins with over 12,000 atoms. According to Jerry Chow, CTO of Quantum-Centric Supercomputing at IBM, the success underscores the transition of quantum machines from experimental novelties to practical scientific tools. The collaboration now aims to refine data transfer speeds between quantum and classical resources, moving toward a workflow that fusion energy developers can eventually use to design and verify reactor materials in real-time.




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