论文标题
减少量子数据，并应用于视频分类
Quantum Data Reduction with Application to Video Classification
论文作者
论文摘要
我们使用混合算法研究了一种量子视频分类方法。量子古典步骤在视频数据集上执行数据减少，而量子步骤（仅可以访问还原数据集）将视频分类为K类之一。我们使用符号视频验证该方法，并证明减少的数据集包含足够的信息，可以使用量子分类过程成功地对数据进行分类。 提出的数据减少方法展示了一种减轻视频分类问题量子计算机的“数据加载”问题的方法。数据加载是一个巨大的瓶颈，因为没有牺牲量子计算的许多好处，没有已知的有效技术来执行该任务。
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.
