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This research demonstrates how Fourier Neural Operators can serve as efficient surrogate simulators for molecular dynamics, bypassing the computational bottlenecks that limit conventional optimization methods. By encoding physical constraints directly into the model, the team achieved high-precision control sequences for hydronium ions, enabling the exploration of state spaces previously inaccessible to standard techniques.

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UCLA & Caltech use quantum-enhanced AI on NVIDIA GPUs to steer molecules

Quantum ZeitgeistSeptember 17, 20265 min read

UCLA’s NarangLab & Caltech, with NVIDIA, used a Fourier Neural Operator—a form of quantum-informed AI—to learn molecular dynamics & design control sequences.

Researchers at UCLA’s NarangLab, Caltech, and NVIDIA have developed a Fourier Neural Operator capable of designing control sequences for molecules in state spaces previously inaccessible to conventional methods. This work focuses on hydronium (H₃O⁺) molecules, whose sensitive inversion transitions offer a potential pathway to probe for physics beyond the Standard Model. “We need a model that has actually learned the underlying physics,” explains the UCLA group, and this new platform is being highlighted at IEEE Quantum Week in Toronto as part of NVIDIA’s launch of an open, programmable platform for fault-tolerant…

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