Machine learning assisted measurement of local topological invariants

TL;DR

This paper demonstrates that machine learning can non-invasively identify local topological invariants in quantum systems using local density of states data, aiding topological material analysis.

Contribution

It introduces a neural network approach to approximate local topological markers from experimental data, enabling non-invasive topological phase detection in solid state systems.

Findings

Neural network accurately distinguishes topological phases.

Method works even without direct transport measurements.

Applicable to composite and complex systems.

Abstract

The continuous effort towards topological quantum devices calls for an efficient and non-invasive method to assess the conformity of components in different topological phases. Here, we show that machine learning paves the way towards non-invasive topological quality control. To do so, we use a local topological marker, able to discriminate between topological phases of one-dimensional wires. The direct observation of this marker in solid state systems is challenging, but we show that an artificial neural network can learn to approximate it from the experimentally accessible local density of states. Our method distinguishes different non-trivial phases, even for systems where direct transport measurements are not available and for composite systems. This new approach could find significant use in experiments, ranging from the study of novel topological materials to high-throughput automated material design.