How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing
This is an incremental review discussing potential applications for researchers in microscopy and nanotechnology.
The paper explores how artificial intelligence and quantum computing can enhance scanning probe microscopy by automating experiments, improving sample region selection, and elucidating structure-property relationships, potentially increasing efficiency and accuracy.
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.