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Researchers at UCLA and Caltech have successfully employed a Fourier Neural Operator to simulate quantum dynamics for hydronium molecules, enabling precise control sequences that bypass traditional computational bottlenecks. This physics-informed approach demonstrates how AI can effectively model complex state spaces in quantum systems where conventional optimization methods fail. The work highlights the growing utility of neural operators as surrogate models for accelerating research into fundamental physics and molecular design.

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

Quantum ZeitgeistSeptember 15, 20265 min read

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

Researchers at UCLA and Caltech, working with NVIDIA, have developed a new approach to controlling molecules using artificial intelligence. The team trained a Fourier Neural Operator to learn the quantum dynamics of hydronium (H₃O⁺), a molecule whose subtle behavior may hold clues to physics beyond the Standard Model. This allows the design of control protocols for molecules in state spaces previously too complex for conventional optimization methods. “We need a model that has actually learned the underlying physics,” explains the UCLA NarangLab group, highlighting the limitations of large language models in…

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