Neural Operator Quantum State: A Foundation Model for Quantum Dynamics
Zihao Qi, Christopher Earls, Yang Peng

TL;DR
This paper introduces Neural Operator Quantum State (NOQS), a foundation model that learns the solution operator for quantum dynamics, enabling rapid predictions of time-evolved states under various protocols without re-training.
Contribution
The work presents NOQS as a novel foundation model that generalizes quantum dynamics across protocols, outperforming traditional methods that require re-computation for each new protocol.
Findings
NOQS accurately predicts quantum states for unseen protocols.
It generalizes to out-of-distribution driving functions.
The model can be transferred across different temporal resolutions.
Abstract
Capturing the dynamics of quantum many-body systems under time-dependent driving protocols is a central challenge for numerical simulations. Existing methods such as tensor networks and time-dependent neural quantum states, however, must be re-run for every protocol. In this work, we introduce the Neural Operator Quantum State (NOQS) as a foundation model for quantum dynamics. Rather than solving the Schr\"odinger equation for individual trajectories, our approach aims to \emph{learn the solution operator} that maps entire driving protocols to time-evolved quantum states. Once trained, the NOQS predicts time evolution under unseen protocols in a single forward pass, requiring no additional optimization. We validate NOQS on the two-dimensional Ising model with time-dependent longitudinal and transverse fields, demonstrating accurate prediction not only for unseen in-distribution…
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Taxonomy
TopicsQuantum many-body systems · Quantum Computing Algorithms and Architecture · Machine Learning in Materials Science
