Mapping quantum Hall edge states in graphene by scanning tunneling microscopy

Johnsen, Tjorven; Schattauer, Christoph; Samaddar, Sayanti; Weston, Astrid; Hamer, Matthew J.; Watanabe, Kenji; Taniguchi, Takashi; Libisch, Florian; Morgenstern, Markus

doi:10.1103/physrevb.107.115426

Phys. Rev. B

2023

DOI: 10.1103/physrevb.107.115426

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Mapping quantum Hall edge states in graphene by scanning tunneling microscopy

Tjorven Johnsen

Christoph Schattauer

Sayanti Samaddar

et al.

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Cited by 5 publications

References 65 publications

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How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

Pregowska,

Roszkiewicz,

Osial

et al. 2024

Microscopy Res & Technique

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The impact of Artificial Intelligence (AI) is rapidly expanding, revolutionizing both science and society. It is applied to practically all areas of life, science, and technology, including materials science, which continuously requires novel tools for effective materials characterization. One of the widely used techniques is scanning probe microscopy (SPM). SPM has fundamentally changed materials engineering, biology, and chemistry by providing tools for atomic‐precision surface mapping. Despite its many advantages, it also has some drawbacks, such as long scanning times or the possibility of damaging soft‐surface materials. In this paper, we focus on the potential for supporting SPM‐based measurements, with an emphasis on the application of AI‐based algorithms, especially Machine Learning‐based algorithms, as well as quantum computing (QC). It has been found that AI can be helpful in automating experimental processes in routine operations, algorithmically searching for optimal sample regions, and elucidating structure–property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of AI‐based algorithms and QC may have enormous potential to enhance the practical application of SPM. The limitations of the AI‐QC‐based approach were also discussed. Finally, we outline a research path for improving AI‐QC‐powered SPM.Research Highlights Artificial intelligence and quantum computing as support for scanning probe microscopy. The analysis indicates a research gap in the field of scanning probe microscopy. The research aims to shed light into ai‐qc‐powered scanning probe microscopy.

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How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

Pregowska,

Roszkiewicz,

Osial

et al. 2024

Microscopy Res & Technique

View full text Add to dashboard Cite

show abstract

Observation of chiral edge transport in a rapidly rotating quantum gas

Yao,

Chi,

Mukherjee

et al. 2024

Nat. Phys.

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Probing the edge states of Chern insulators using microwave impedance microscopy

Wang,

Wu,

Mogi

et al. 2023

Phys. Rev. B

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Mapping quantum Hall edge states in graphene by scanning tunneling microscopy

Cited by 5 publications

References 65 publications

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

How scanning probe microscopy can be supported by artificial intelligence and quantum computing?

Observation of chiral edge transport in a rapidly rotating quantum gas

Probing the edge states of Chern insulators using microwave impedance microscopy

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