Classical autoencoders are widely used to learn features of input data. To improve the feature learning, classical masked autoencoders extend classical autoencoders to learn the features of the original input sample in the presence of masked-out data. While quantum autoencoders exist, there is no design and implementation of quantum masked autoencoders that can leverage the benefits of quantum computing and quantum autoencoders. In this paper, we propose quantum masked autoencoders (QMAEs) that can effectively learn missing features of a data sample within quantum states instead of classical embeddings. We showcase that our QMAE architecture can learn the masked features of an image and can reconstruct the masked input image with improved visual fidelity in MNIST-family images. Experimental evaluation highlights that QMAE can significantly outperform (12.86% on average) in classification accuracy compared to state-of-the-art quantum autoencoders in the presence of masks.

翻译：经典自编码器被广泛用于学习输入数据的特征。为提升特征学习能力，经典掩码自编码器在存在掩码数据的情况下，将经典自编码器扩展为学习原始输入样本的特征。尽管目前已存在量子自编码器，但尚无能够利用量子计算及量子自编码器优势的量子掩码自编码器设计与实现。本文提出量子掩码自编码器（QMAEs），该模型能够高效学习量子态中数据样本的缺失特征，而非依赖于经典嵌入。我们证明，所提出的QMAE架构可学习图像的掩码特征，并在MNIST系列图像中实现具有更高视觉保真度的掩码输入图像重构。实验评估表明，在存在掩码的情况下，QMAE在分类准确率上相较于现有最优量子自编码器具有显著优势（平均提升12.86%）。