Segmentation problems constitute a large part of the tasks in image processing. Solutions to difficult segmentation problems are typically complex and tailor-made. Machine and statistical learning techniques have higher potential for a general-purpose solution. However, training data is difficult to obtain, and as images get larger, more complex models with increasing computational costs are needed.
Therefore, I am researching, testing, and developing quantum computing simulation or learning methods as an alternative. I am also investigating how the potential of quantum computing can be used to improve or simplify machine learning methods. Finally, the methods are applied to structures of materials that are mapped by the FIB-REM serial sectioning technique 3D at the nanoscale.