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This collaboration demonstrates a significant leap in quantum hardware efficiency by using Fourier Neural Operators to accelerate the synthesis of control pulse sequences by a factor of 10⁷. By replacing traditional numerical solvers with a differentiable machine learning surrogate, the framework reduces optimization time from hours to minutes while maintaining high success rates for state preparation. This advancement directly bridges advanced AI modeling techniques with practical quantum computing infrastructure, specifically within NVIDIA's CUDA-Q ecosystem.

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UCLA, Caltech, and NVIDIA Develop Fourier Neural Operator for Quantum Control Sequence Synthesis

Quantum Computing ReportSeptember 15, 20262 min read

A research collaboration involving UCLA, Caltech, and NVIDIA has developed a machine-learning framework utilizing a Fourier Neural Operator (FNO) for the inverse design of quantum control pulse sequences. This FNO-based method significantly accelerates the synthesis of quantum control sequences, achieving a 10⁷x speedup and reducing computation time from 10 hours to 10-20 minutes, with an 86.2% success rate in preparing target quantum states. The framework, built on NVIDIA's CUDA-Q platform, offers a differentiable approach for optimizing laser pulse parameters in molecular quantum dynamics. The post UCLA, Caltech, and NVIDIA Develop Fourier Neural Operator for Quantum Control Sequence Synthesis appeared first on Quantum Computing Report.

FNO-based inverse-design pipeline for quantum state preparation. (a) Quantum control for molecular state preparation. (b) Propagating single-pulse molecular dynamics with the FNO. A multi-institution research collaboration led…

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