Unsupervised learning of correlated quantum dynamics on disordered lattices

TL;DR

This paper introduces a deep learning approach using GANs to learn and generate correlated quantum dynamics on disordered lattices without supervision, revealing disorder as a key control parameter and enabling accelerated learning of complex quantum problems.

Contribution

The authors develop an unsupervised deep learning algorithm that captures quantum correlations and identifies disorder as a control parameter, advancing quantum simulation methods.

Findings

Algorithm successfully generates physically correct new quantum states.

Disorder identified as the key control parameter in the latent space.

Network accelerates learning of more complex quantum problems.

Abstract

Quantum particles co-propagating on disordered lattices develop complex non-classical correlations due to an interplay between quantum statistics, inter-particle interactions, and disorder. Here we present a deep learning algorithm based on Generative Adversarial Networks, capable of learning these correlations and identifying the physical control parameters in a completely unsupervised manner. After one-time training on a data set of unlabeled examples, the algorithm can generate, without further calculations, a much larger number of unseen yet physically correct new examples. Furthermore, the knowledge distilled in the algorithm's latent space identifies disorder as the relevant control parameter. This allows post-training tuning of the level of disorder in the generated samples to values the algorithm was not explicitly trained on. Finally, we show that a trained network can accelerate the learning of new, more complex problems. These results demonstrate the ability of neural networks to learn the rules of correlated quantum dynamics in an unsupervised manner and offer a route to their use in quantum simulations and computation.