# Machine Learning Topological Phases with a Solid-state Quantum Simulator

## TL;DR

This paper demonstrates that convolutional neural networks can be trained on experimental data from a solid-state quantum simulator to accurately identify three-dimensional chiral topological insulator phases, showcasing machine learning's potential in topological phase detection.

## Contribution

It introduces a novel application of machine learning, specifically CNNs, for experimental identification of topological phases in solid-state quantum systems, advancing the field's methodology.

## Key findings

- CNNs successfully identify topological phases from experimental data
- Machine learning enhances detection of topological phenomena
- Experimental validation of CNNs in quantum materials

## Abstract

We report an experimental demonstration of a machine learning approach to identify exotic topological phases, with a focus on the three-dimensional chiral topological insulators. We show that the convolutional neural networks---a class of deep feed-forward artificial neural networks with widespread applications in machine learning---can be trained to successfully identify different topological phases protected by chiral symmetry from experimental raw data generated with a solid-state quantum simulator. Our results explicitly showcase the exceptional power of machine learning in the experimental detection of topological phases, which paves a way to study rich topological phenomena with the machine learning toolbox.