Fourier Neural Operators predict quantum dynamics 10^7x faster than CUDA-Q
AnimaAnandkumar · x · 2026-08-20
A new paper (arXiv:2608.03702) from Anima Anandkumar's team argues LLMs lack innate physical-world understanding, and that the complexity of quantum dynamics grows exponentially with Hilbert space dimension, demanding specialized operator-learning tools.
The paper introduces a Fourier Neural Operator (FNO) framework for learning high-dimensional molecular quantum dynamics and accelerating inverse design of control protocols:
- FNO predicts molecular population dynamics up to 10^7x faster than GPU-accelerated numerical propagation with CUDA-Q Dynamics
- The differentiable surrogate powers the FNO stochastic pulse-measurement planner (FNO-SPMP) to purify an initially mixed Boltzmann distribution
- Demonstrated on an 888-dimensional subspace of the hydronium molecule at 20 K: target-state population of 0.98 and sequence success rates up to 86.2%
- Versus a reinforcement-learning baseline, FNO-SPMP nearly doubles success rate with roughly half the control pulses, cutting pulse-sequence generation from 10 hours to 10-20 minutes
The results show operator-learning surrogates enable inverse design in quantum systems whose Hilbert spaces are too large for conventional methods.
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