How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866910505359114240 |
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| author | Pregowska, Agnieszka Roszkiewicz, Agata Osial, Magdalena Giersig, Michael |
| author_facet | Pregowska, Agnieszka Roszkiewicz, Agata Osial, Magdalena Giersig, Michael |
| contents | We focus on the potential possibilities for supporting Scanning Probe Microscopy measurements, emphasizing the application of Artificial Intelligence, especially Machine Learning as well as quantum computing. It turned out that Artificial Intelligence can be helpful in the experimental processes automation in routine operations, the algorithmic search for good sample regions, and shed light on the structure property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of Artificial Intelligence based algorithms and quantum computing may have a huge potential to increase the practical application of Scanning Probe Microscopy. The limitations were also discussed. Finally, we outline a research path for the improvement of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_19397 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing Pregowska, Agnieszka Roszkiewicz, Agata Osial, Magdalena Giersig, Michael Neurons and Cognition Materials Science Artificial Intelligence Quantum Physics We focus on the potential possibilities for supporting Scanning Probe Microscopy measurements, emphasizing the application of Artificial Intelligence, especially Machine Learning as well as quantum computing. It turned out that Artificial Intelligence can be helpful in the experimental processes automation in routine operations, the algorithmic search for good sample regions, and shed light on the structure property relationships. Thus, it contributes to increasing the efficiency and accuracy of optical nanoscopy scanning probes. Moreover, the combination of Artificial Intelligence based algorithms and quantum computing may have a huge potential to increase the practical application of Scanning Probe Microscopy. The limitations were also discussed. Finally, we outline a research path for the improvement of the proposed approach. |
| title | How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing |
| topic | Neurons and Cognition Materials Science Artificial Intelligence Quantum Physics |
| url | https://arxiv.org/abs/2406.19397 |