Hitesh Changlani to model electrons in quantum materials

Florida State University physicist Hitesh Changlani will model the behavior of electrons in quantum materials with a $2 million Department of Energy grant shared with Brown University’s Kemp Plumb. The research will use neutron scattering to observe how these materials, found in technologies from smartphones to natural processes like photosynthesis, respond to changing conditions. “Quantum mechanics is all around you,” Changlani said, explaining the team aims to better understand and control these materials for more efficient technologies by observing interacting electrons’ responses.

Modeling Out-of-Equilibrium Quantum Matter with Neutron Scattering

The $2 million Department of Energy grant awarded to Hitesh Changlani and Kemp Plumb will facilitate the application of neutron scattering to the study of out-of-equilibrium quantum matter, a relatively unexplored area of condensed matter physics. This technique, involving the deflection and energy loss of neutrons fired at a material, allows researchers to visualize the arrangement of electron spins in real-time as the material undergoes change.

Changlani explained the process by drawing a parallel to medical imaging; “If you fracture your arm and look at an X-ray of it every day, you’ll see how your arm is healing,” he said, describing how the team will observe evolving magnetic systems. The research focuses on materials exhibiting behavior outside of equilibrium, where competing forces are not balanced, and seeks to understand how to manipulate these materials into useful quantum states.

Neutron scattering has previously proven effective in characterizing materials in equilibrium, including high-temperature cuprate superconductors, quantum spin liquids and altermagnets, all relevant to technologies like quantum computers and power grids. However, extending this method to dynamic, out-of-equilibrium systems presents a new challenge and opportunity for discovery. This collaboration between Changlani and Plumb, which began in 2016 at Johns Hopkins University, combines expertise from both Florida State University and Brown University, with nearly $800,000 of the grant funding research at FSU and the National High Magnetic Field Laboratory.

Simulations created by Changlani will use high-performance computing resources at FSU’s Research Computing Center, complementing the neutron scattering data captured in collaboration with ORNL’s Neutron Scattering Division.

Neutron scattering has illuminated how materials like high-temperature cuprate superconductors, quantum spin liquids and altermagnets, which are relevant to important technologies like quantum computers, power grids and storage devices, work in equilibrium.

Hitesh Changlani, Associate Professor of Physics at Florida State University

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Understanding how to control the quantum states of materials is important for advancing technologies reliant on their unique properties, with applications potentially extending to more efficient energy transfer and storage. “Developments across the field have helped us understand how we can steer quantum materials to desirable states, like superconducting states, so we can use them more efficiently in modern technologies,” Changlani said. The researchers are particularly interested in non-equilibrium physics, a field that has gained prominence over the last 20 years, and the challenges of applying neutron scattering to systems that are not static.

Quantum mechanics is all around you: in your phone, in superconducting materials, in magnets and more.

Hitesh Changlani, Associate Professor of Physics at Florida State University
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Ivy Delaney

Ivy Delaney has been working with neural networks and machine learning since the mid-nineties, back when a couple of hidden layers and a long afternoon of training counted as ambitious. She has watched the field go from academic curiosity to the thing quietly running underneath everything, and she brings that long view to quantum computing. For Quantum Zeitgeist she covers the ground where the two fields meet. That means quantum machine learning and the variational algorithms it leans on, and it also means the less glamorous but more interesting story of classical machine learning already doing real work inside quantum machines, decoding error-correcting codes, calibrating noisy hardware and learning the error models that simulators depend on. She writes about the hardware those algorithms have to run on too, and about the post-quantum cryptography scramble that the same hardware has set off. Her stories typically start with the paper, whether that is peer-reviewed work, conference proceedings or an arXiv preprint, with the source linked so you can hold a claim up against the research it came from. She is unimpressed by benchmarks that will not say what they beat, and by demonstrations that only work in the press release.

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