removing highlighted changes for issue #22

Author: marekyggdrasil

manuscript.tex

@@ -558,7 +558,7 @@ \section{Discussions}
 where $N$ is number of samples, $M$ is number of randomly sampled cluster vectors, 
 and $L$ is number of the shifts of cluster vectors.
 Due to wide use of classical SOFMs in different areas of modern research and technology, 
-this can give opportunities for the use of QASOFM in practical applications in near term, outperforming classical algorithms. \hl{In addition, as our algorithm performs the Hamming distance calculation, it has potential to enhance any classical algorithm that relies on calculating distances between data entries of vector form. In machine learning, data science, statistics and optimization, distance is a common way of representing similarity, calculating it between large data sets is common procedure and our circuit could potentially enhance other distance-based algorithms as long as exact distance is not required, but when knowledge of nearest vectors is sufficient.}
+this can give opportunities for the use of QASOFM in practical applications in near term, outperforming classical algorithms. In addition, as our algorithm performs the Hamming distance calculation, it has potential to enhance any classical algorithm that relies on calculating distances between data entries of vector form. In machine learning, data science, statistics and optimization, distance is a common way of representing similarity, calculating it between large data sets is common procedure and our circuit could potentially enhance other distance-based algorithms as long as exact distance is not required, but when knowledge of nearest vectors is sufficient.