As a ubiquitous aspect of modern information technology, data compression has a wide range of applications. Therefore, a quantum autoencoder which can compress quantum information into a low-dimensional space is fundamentally important to achieve automatic data compression in the field of quantum information. Such a quantum autoencoder can be implemented through training the parameters of a quantum device using classical optimization algorithms. In this paper, we demonstrate the condition of achieving a perfect quantum autoencoder and theoretically prove that a quantum autoencoder can losslessly compress high-dimensional quantum information into a low-dimensional space (also called latent space) if the number of maximum linearly independent vectors from input states is no more than the dimension of the latent space. Also, we experimentally realize a universal two-qubit unitary gate and design a quantum autoencoder device by applying a machine learning method. Experimental results demonstrate that our quantum autoencoder is able to compress two two-qubit states into two one-qubit states. Besides compressing quantum information, the quantum autoencoder is used to experimentally discriminate two groups of nonorthogonal states.

中文翻译：

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量子自动编码器的实现，用于量子数据的无损压缩

作为现代信息技术无处不在的方面，数据压缩具有广泛的应用范围。因此，能够将量子信息压缩到低维空间的量子自动编码器对于实现量子信息领域中的自动数据压缩至关重要。可以通过使用经典的优化算法训练量子设备的参数来实现这种量子自动编码器。在本文中，我们证明了实现完美的量子自编码器的条件，并在理论上证明了：如果自输入状态不超过潜在空间的维数。也，我们实验性地实现了通用的两比特unit门，并通过应用机器学习方法设计了量子自动编码器。实验结果表明，我们的量子自编码器能够将两个两个量子比特的状态压缩为两个一个量子比特的状态。除了压缩量子信息外，量子自编码器还用于通过实验区分两组非正交态。