Observing a topological phase transition with deep neural networks from experimental images of ultracold atoms

TL;DR

This paper demonstrates that deep neural networks can successfully identify topological phase transitions from noisy experimental images of ultracold atoms, matching traditional methods but with higher noise tolerance.

Contribution

The study introduces a deep learning approach to detect topological phase transitions directly from experimental data, even at low signal-to-noise ratios, and visualizes the features used for classification.

Findings

Successfully identified topological phase transitions from low SNR data.

Predicted phase diagram consistent with conventional high SNR analysis.

CNN uses conventional physical indicators like spin imbalance for classification.

Abstract

Although classifying topological quantum phases have attracted great interests, the absence of local order parameter generically makes it challenging to detect a topological phase transition from experimental data. Recent advances in machine learning algorithms enable physicists to analyze experimental data with unprecedented high sensitivities, and identify quantum phases even in the presence of unavoidable noises. Here, we report a successful identification of topological phase transitions using a deep convolutional neural network trained with low signal-to-noise-ratio (SNR) experimental data obtained in a symmetry-protected topological system of spin-orbit-coupled fermions. We apply the trained network to unseen data to map out a whole phase diagram, which predicts the positions of the two topological phase transitions that are consistent with the results obtained by using the conventional method on higher SNR data. By visualizing the filters and post-convolutional results of the convolutional layer, we further find that the CNN uses the same information to make the classification in the system as the conventional analysis, namely spin imbalance, but with an advantage concerning SNR. Our work highlights the potential of machine learning techniques to be used in various quantum systems.