Deep Learning of Quantum Many-Body Dynamics via Random Driving

TL;DR

This paper demonstrates that deep learning models can predict quantum many-body dynamics from observable data under random driving, enabling efficient analysis of complex systems without full quantum state information.

Contribution

The work introduces a neural network approach trained solely on observable expectation values to predict quantum dynamics, including extrapolation to longer times and larger system sizes.

Findings

Neural network accurately predicts dynamics for unseen driving trajectories.

The approach works with data from spin models and can be applied to experimental systems.

The model extrapolates to longer times and infinite system size.

Abstract

Neural networks have emerged as a powerful way to approach many practical problems in quantum physics. In this work, we illustrate the power of deep learning to predict the dynamics of a quantum many-body system, where the training is \textit{based purely on monitoring expectation values of observables under random driving}. The trained recurrent network is able to produce accurate predictions for driving trajectories entirely different than those observed during training. As a proof of principle, here we train the network on numerical data generated from spin models, showing that it can learn the dynamics of observables of interest without needing information about the full quantum state. This allows our approach to be applied eventually to actual experimental data generated from a quantum many-body system that might be open, noisy, or disordered, without any need for a detailed understanding of the system. This scheme provides considerable speedup for rapid explorations and pulse optimization. Remarkably, we show the network is able to extrapolate the dynamics to times longer than those it has been trained on, as well as to the infinite-system-size limit.