论文标题
关于量子自动编码器的压缩率：控制设计，数值和实验实现
On compression rate of quantum autoencoders: Control design, numerical and experimental realization
论文作者
论文摘要
旨在在低维潜在空间中压缩量子信息的量子自动编码器位于量子信息领域的自动数据压缩的核心。在本文中，我们为给定量子自动编码器建立了压缩率的上限，并提出了一种学习控制方法，以训练自动装码器以达到最大压缩率。理论上使用特征分解和基质分化来证明压缩速率的上限，这取决于输入状态的密度矩阵表示的特征值。提出了2量和3 Q量系统的数值结果，以演示如何训练量子自动编码器以实现理论上最大的压缩，并比较使用不同的机器学习算法的训练性能。说明了使用量子光学系统的量子自动编码器的实验结果，以将两个2 Q Q Q Q Qubit的状态压缩为两个1 Quit状态。
Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper bound of the compression rate for a given quantum autoencoder and present a learning control approach for training the autoencoder to achieve the maximal compression rate. The upper bound of the compression rate is theoretically proven using eigen-decomposition and matrix differentiation, which is determined by the eigenvalues of the density matrix representation of the input states. Numerical results on 2-qubit and 3-qubit systems are presented to demonstrate how to train the quantum autoencoder to achieve the theoretically maximal compression, and the training performance using different machine learning algorithms is compared. Experimental results of a quantum autoencoder using quantum optical systems are illustrated for compressing two 2-qubit states into two 1-qubit states.
