基于量子自编码器的量子混合态压缩

Quantum Compression of Mixed States Based on Quantum Autoencoder

量子数据的大小会影响量子计算机的处理速度,量子数据压缩可以减小量子计算机的存储和运行时间,提高量子设备利用率,并减少对容错能力的要求。量子数据压缩技术推动了量子信息和量子计算的发展。量子自编码器是一种提取输入态中最相关特征的前馈型神经网络,后被用作压缩量子数据,来对纯态进行压缩。论文研究了量子自编码器对混合量子态的压缩效果。基于量桨平台(Paddle Quantum),对量子混合态压缩进行了模拟仿真,结果显示量子自编码器对量子混合态有很好的压缩效果。

The size of quantum data affects the processing speed of quantum computer.By compressing quantum data,the storage volume and running time of quantum computer can be reduced,and the requirements for fault tolerance can be reduced.The emergence of quantum data compression promotes the development of quantum information and quantum computation.Quantum au⁃toencoder is a feedforward neural network that extracts the most relevant features of input states,and it has been used to compress quantum pure states.In this paper,the effects of quantum autoencoder for compressing quantum mixed states is studied.The com⁃pression process is simulated by using paddle quantum platform.The results show that the quantum autoencoder has a good compres⁃sion effect on mixed states.