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

UCLA, Caltech, and NVIDIA Develop Fourier Neural Operator for Quantum Control Sequence Synthesis FNO-based inverse-design pipeline for quantum state preparation. (a)Quantum control for molecular statepreparation. (b)Propagating single-pulse molecular dynamics with the FNO. A multi-institution research collaboration led by UCLA (NarangLab), Caltech, and NVIDIA has introduced a machine-learning framework to automate the inverse design of quantum control pulse sequences. Detailed in a preprint published on arXiv (arXiv:2608.03702) and presented at IEEE Quantum Week 2026, the method utilizes a Fourier Neural Operator (FNO) to learn high-dimensional molecular quantum dynamics, replacing numerical differential equation solvers inside optimal control loops. The FNO surrogate was trained on GPU-accelerated quantum state propagations generated by NVIDIA CUDA-Q Dynamics across an 888-dimensional Hilbert space modeling the trapped hydronium ion (H₃O⁺). Incorporating physics-informed frequency detuning embeddings and polarization symmetry constraints (σ⁺ and σ⁻ channels), the FNO surrogate predicts population trajectories across Raman sideband pulse windows up to ~10⁷× faster than accelerated GPU numerical propagation solvers. The surrogate is fully differentiable, enabling direct gradient-based optimization of continuous laser pulse parameters. [ FNO-SPMP Control Performance vs.

Reinforcement Learning Baseline ]Control MetricFourier Neural Operator (FNO-SPMP)Standard Reinforcement Learning (RL)Target State Fidelity & Success• Target Population Density: 0.98• Preparation Success Rate: Up to 86.2%• ~43% Success Rate Baseline• Higher Residual Thermal EntropySequence Overhead• ~50% Reduction in Required Pulse Count• 2× Larger Optical Pulse OverheadSynthesis Compute Latency• 10 to 20 Minutes (Differentiable FNO)• ~10 Hours (Discrete RL Search) Built on top of the differentiable FNO, the team implemented a stochastic pulse-measurement planner (FNO-SPMP) to navigate thermal population distributions at 20 K. On H₃O⁺, the framework generated pulse sequences that funneled thermal populations into single rotational/hyperfine states with an 86.2% success rate, reducing sequence synthesis latency from 10 hours down to 10–20 minutes. The methodology establishes an operator-learning paradigm within NVIDIA’s CUDA-Q platform to synthesize pulse-level control for molecular spectroscopy and complex QPU gate calibration. Review the technical update via UCLA NarangLab here, examine the peer-reviewed preprint on arXiv here, and read our prior coverage of NVIDIA CUDA-Q Platform Integrations here. September 14, 2026 Mohamed Abdel-Kareem2026-09-14T23:04:17-07:00 Leave A Comment Cancel replyComment Type in the text displayed above Δ This site uses Akismet to reduce spam. Learn how your comment data is processed.