  Point set registration is one of the challenging tasks in areas such as
pattern recognition, computer vision and image processing. Efficient
performance of this task has been a hot topic of research due to its widespread
applications. We propose a parameterised quantum circuit learning approach to
point set matching problem. The proposed method benefits from a kernel-based
quantum generative model that: 1) is able to find all possible optimal matching
solution angles, 2) is potentially able to show quantum learning supremacy, and
3) benefits from kernel-embedding techniques and integral probability metrics
for the definition of a powerful loss function. Moreover, the theoretical
framework has been backed up by satisfactory preliminary and proof of concept
experimental results.
