  We investigate a quantum video classification method using a hybrid
algorithm. A quantum-classical step performs a data reduction on the video
dataset and a quantum step -- which only has access to the reduced dataset --
classifies the video to one of k classes. We verify the method using sign
videos and demonstrate that the reduced dataset contains enough information to
successfully classify the data, using a quantum classification process.
  The proposed data reduction method showcases a way to alleviate the "data
loading" 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.
