Quantum Data Reduction with Application to Video Classification
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
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” 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 “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.