  Finding groups of similar image blocks within an ample search area is often
necessary in different applications, such as video compression, image
clustering, vector quantization, and nonlocal noise reduction. A block-matching
algorithm that uses a dissimilarity measure can be applied in such scenarios.
In this work, a measure that utilizes the quantum Fourier transform or the Swap
test based on the Euclidean distance is proposed. Experiments on small cases
with ideal and noisy simulations are implemented. In the case of the Swap test,
the IBM and IonQ quantum devices have been used, demonstrating potential for
future near-term applications.
