Quantum computing models tritium production, advancing nuclear fusion reactor designs

Quantum computing wielded to create extremely rare material critical to nuclear fusion

Nuclear fusion inches closer after scientists combine supercomputing, AI and quantum computing to blueprint a way to create more tritium. Using a quantum computer alongside a supercomputer, scientists have developed a breakthrough pathway for modeling the physics inside a fusion reactor. The world-first experiment could help clear a path to developing clean, abundant nuclear power and solving the global energy crisis, the researchers said. Using hybrid quantum computing and artificial intelligence (AI) methods, scientists with IBM and Oak Ridge National Laboratory (ORNL) have blueprinted how to make tritium, an extremely rare isotope of hydrogen that's critical to the fusion process. Although their research -- uploaded June 29 to the preprint server arXiv -- has not been peer-reviewed, the researchers say it's the first time that different kinds of computing elements have come together to propose the most effective way to create this material. Fusion reactors are experimental power sources that create energy by fusing atomic nuclei. The heat produced in the subsequent nuclear reaction is then harnessed as energy. This method produces no carbon byproducts or long-lived radioactive waste, making it one of the cleanest potential forms of mass energy production. It's projected that, at scale, a single fusion reactor could produce about 4 million times as much energy as a coal-burning facility and around four times the amount of energy as a modern nuclear fission reactor. Current attempts at building a viable fusion reactor have resulted in numerous laboratory experiments that prove the technology works, with magnetic confinement reactors, such as tokamaks, widely considered the front-runner. But many engineering challenges remain before the first commercial reactors could come online. Turning seawater into fuel The base fuel for nuclear fusion reactors is a hydrogen isotope called deuterium, which is commonly found in seawater. It's estimated that there are 33 grams of deuterium in every cubic meter of seawater. But deuterium is only half of the equation. Nuclear fusion also requires tritium -- a heavier hydrogen isotope -- and the fusion released from just 1 gram (0.04 ounces) of deuterium-tritium fuel equals the energy from about 2,400 gallons (9,100 liters) of oil, according to the U.S. Department of Energy. Unfortunately, tritium, a radioactive isotope, is extremely rare; only 44 pounds (20 kilograms) of it is produced on Earth each year, and its 12-year half-life makes it difficult to use in nuclear power plants. Instead, scientists must painstakingly produce tritium in nuclear reactors by bombarding lithium atoms with neutrons. It's then superheated and bound with powerful magnets into a whirling ring of plasma within a tokamak, a special fusion chamber designed to shape and heat plasma using magnetic fields. Scientists add more deuterium and then bash the tritium and deuterium together, causing them to fuse into helium. The force of this reaction creates heat that's converted into energy. The current bottleneck lies in creating enough tritium to sustain fusion long enough to produce energy. But modeling the particle physics and chemical reactions involved in the tritium-creation process has proved beyond the capabilities of classical supercomputers. In the new study, however, scientists say they have addressed this bottleneck by simulating nine molecular configurations of a liquid salt that contains fluorine, lithium and beryllium (FLiBe) -- one of the leading candidate materials for extracting tritium. This is the first time quantum computers have been used to model reactions inside a fusion reactor. If perfected, FLiBe could provide a near-limitless source of fuel for nuclear fusion reactors, they said, but the chemistry involved is incredibly complex. Demystifying complex chemistry A "blanket of molten salt" made of FLiBe is wrapped around the nuclear reaction inside a fusion reactor, IBM researchers told Live Science. This provides both a fuel source and a thermal shield for the device. To create enough tritium, the researchers had to calculate the physics involved while a process called "neutron bombardment" constantly altered the blanket's chemistry. Designing a salt that holds up under competing demands and keeps releasing tritium is a key problem in building this kind of reactor. "If tritium grabs onto fluorine in the salt, it forms tritium fluoride, which is corrosive and stubborn to remove," the researchers explained. "If it binds to another tritium atom to form a gas, it bubbles out on its own. Predicting which way the reaction goes means modeling the interaction between tritium and the salt with high precision and accuracy that is challenging for classical methods." Because no ordinary computer can perform the necessary calculations, the team used a combination of AI running on the Frontier supercomputer at ORNL, alongside quantum computing algorithms running on an IBM Quantum Heron quantum processing unit (QPU) in New York. The resulting workflow demonstrated a proof of concept for offloading complex chemistry computations to a quantum computer. That workflow relied on a technique called wave-function-based embedding, which fragments the calculation into easier-to-calculate clusters, the scientists said in the study. They used classical computers to solve the smaller clusters and passed off the more difficult chunks to a quantum computer. The classical computers then stitched the molecule back together. This is a method that study co-author Kenneth Merz, a biochemist and principal investigator at Cleveland Clinic Research, pioneered in previous research. Earlier this year, in collaboration with IBM and the Japanese national research institute RIKEN, he used quantum computers to calculate the structure of a 12,635-atom protein. Fusing quantum and AI In the new study, the researchers tested their model against known molecular configurations that were already solved by a nonhybrid classical system and determined that the accuracy was maintained with the addition of quantum computations. This proof of concept should serve as a direct pathway for scaling the models used to predict tritium production within fusion reactors, potentially solving what may be the biggest hurdle to large-scale fusion energy production. The broader workflow the scientists outlined in a technical blog post involved three stages. First, AI agents proposed and screened many candidate salts from the ORNL database, and for each candidate, calculations estimated various qualities in the tritium breeding process, including how much fuel the salt would make under neutron bombardment. The most promising salts then went to a supercomputer, which modeled them atom by atom, using the density functional theory (DFT) process to approximate how a molecule's electrons would arrange themselves. These are expensive simulations, so the scientists used "AI stand-ins" trained to reproduce the physics to run them fast enough to be useful. The third stage brought in the quantum computer to figure out where the tritium would bind, which is a shortcoming for DFT. In the future, the research team will model larger molten-salt systems and study more molecular configurations before evaluating whether AI can slash the time it will take to find a promising molten-salt material. The wider aim, the scientists told Live Science, is to build a reliable computational pathway for fusion-materials discovery that can help researchers predict how well a blanket material breeds tritium, whether that tritium can be recovered, and how the material may perform in the extreme environment of a fusion reactor. Can you match these ancient devices to their pictures? Find out with our computing quiz!

Scientists make first-known quantum computations of fusion material

Scientists from Oak Ridge National Laboratory (ORNL), Cleveland Clinic and IBM have used quantum computers to calculate molecular configurations of a key fusion fuel material, marking what the team says is the first known demonstration of its kind. The work focuses on FLiBe, a molten salt made of fluorine, lithium, and beryllium that is considered one of the leading materials for producing and extracting tritium inside future fusion reactors. Tritium is an extremely scarce hydrogen isotope needed to fuel most proposed fusion power plants. Researchers calculated nine molecular configurations of FLiBe using quantum-centric supercomputing, combining quantum and classical computers to solve a problem that becomes increasingly difficult for conventional computing alone. The results could help scientists better understand how tritium interacts with molten salt at the atomic level, providing insights needed to optimize future fusion reactor designs and improve tritium production. Securing enough tritium remains one of the biggest challenges facing commercial fusion energy. Because the isotope occurs only in tiny amounts naturally, future reactors are expected to generate their own tritium using materials such as FLiBe inside a surrounding molten salt blanket. Quantum computers are particularly suited for studying the behavior of electrons that determine how atoms bond and interact. In this work, researchers applied the same quantum-centric computing techniques previously used to simulate proteins containing 12,635 atoms, extending the approach from biology into materials science. "In order to demonstrate the capabilities catalyzed by the Genesis Mission, we have built a team of leading experts across seven DOE national labs, four universities, three industry partners and Cleveland Clinic to pursue a multi-pronged discovery cycle aimed at optimizing tritium production in molten salt fusion blanket materials," said Tom Beck, Section Head for Science Engagement in the Computing and Computational Sciences Directorate at ORNL. "Quantum computers, such as those built by IBM and enhanced by AI and exascale computing, are key tools that accelerate the discovery and design cycles needed to produce sufficient tritium to fuel fusion reactors." The scientists used quantum-centric supercomputing, allowing quantum processors and classical computers to work together. Quantum circuits handled the parts of the calculations best suited for quantum hardware, while conventional computing completed the remaining tasks. This approach enabled the team to calculate the electronic structure of FLiBe with and without tritium and determine how strongly different molecular configurations bind the fuel. The researchers said these atomic-scale interactions are difficult to capture accurately using classical approximation methods alone. "This work builds on our advances in simulating complex biological systems at scale, including proteins spanning 12,635 atoms and extends those techniques into materials science to explore fusion-relevant systems with greater accuracy and efficiency," said Kenneth Merz, PhD, corresponding author and staff scientist at Cleveland Clinic. "Bringing quantum, AI, and classical computing together is essential to tackling our society's most fundamental scientific challenges - unlocking capabilities which none of these paradigms can access alone," said Jerry Chow, CTO of Quantum-Centric Supercomputing at IBM. The collaboration will next focus on reducing the time needed to transfer data between quantum and classical computers while expanding the size of molecular systems that can be modeled. Researchers ultimately hope fusion developers can use the workflow to design and evaluate their own reactor materials. The study was published on arXiv.