How scanning probe microscopy can be supported by Artificial Intelligence and quantum computing

TL;DR

This paper explores how Artificial Intelligence and quantum computing can enhance Scanning Probe Microscopy by automating experiments, improving accuracy, and expanding practical applications, while discussing limitations and future research directions.

Contribution

It introduces the integration of AI and quantum computing to support and improve the efficiency and accuracy of Scanning Probe Microscopy techniques.

Findings

AI automates routine operations in microscopy

AI aids in selecting optimal sample regions

Quantum computing enhances the practical application potential

Abstract

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.