Machine learning assisted measurement of local topological invariants

Understand

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.