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28 March 2024 |
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Article overview
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Machine learning assisted measurement of local topological invariants | Marcello D. Caio
; Marco Caccin
; Paul Baireuther
; Timo Hyart
; Michel Fruchart
; | Date: |
10 Jan 2019 | 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. We introduce 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. | Source: | arXiv, 1901.3346 | Services: | Forum | Review | PDF | Favorites |
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