@proceedings {163,
	title = {Quantum Data Reduction with Application to Video Classification},
	journal = {ACM/IEEE Workshop on Quantum Computing},
	year = {2022},
	abstract = {We investigate a quantum data reduction technique with application to video classification. A hybrid quantum-classical step performs data reduction on the video dataset generating "representative{\textquoteright}{\textquoteright} distributions for each video class. These distributions are used by a quantum classification algorithm to firstly reduce the size of the videos and then classify the reduced videos to one of $k$ classes. We verify the method using sign videos and demonstrate that the reduced videos contain enough information to successfully classify them using a quantum classification process. The proposed data reduction method showcases a way to alleviate the {\textquoteleft}{\textquoteleft}data loading{\textquoteright}{\textquoteright} problem of quantum computers for the problem of video classification. Data loading is a huge bottleneck, as there are no known efficient techniques to perform that task without sacrificing many of the benefits of quantum computing.},
	author = {Kostas Blekos and Dimitrios Kosmopoulos}
}
