Deep Learning of Quantum Many-Body Dynamics via Random Driving

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.

References in corpus (5)

• Sequence to Sequence Learning with Neural Networks
• Learning phase transitions by confusion
• Controlled exchange interaction between pairs of neutral atoms in an optical lattice
• Quantum simulation of time-dependent Hamiltonians and the convenient illusion of Hilbert space
• Machine learning vortices at the Kosterlitz-Thouless transition

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