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This collaboration demonstrates how Fourier Neural Operators can serve as high-fidelity surrogate models for quantum dynamics, drastically reducing the computational cost of designing control sequences for complex molecules like hydronium. By bypassing expensive iterative simulations, this approach accelerates the preparation of specific quantum states, a critical step toward probing physics beyond the Standard Model. The work highlights a tangible convergence where AI architectures directly enable practical advances in quantum computing hardware and spectroscopy.
NVIDIA helps UCLA steer molecules with AI trained on quantum physics
UCLA’s NarangLab & NVIDIA researchers used a Fourier Neural Operator to learn molecular quantum dynamics, designing control sequences for complex systems.
Researchers at UCLA’s NarangLab and Caltech, working with NVIDIA, have developed an artificial intelligence model capable of designing control sequences for quantum applications in scenarios exceeding the limits of conventional optimization. The team’s Fourier Neural Operator learns the quantum dynamics of molecules, enabling the identification of control protocols for complex systems like hydronium (H₃O⁺), chosen for its potential to probe physics beyond the Standard Model. This approach replaces computationally expensive simulations with a surrogate model running up to ~10⁷× faster on NVIDIA’s CUDA-Q Dynamics platform. The…
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