Realization of a quantum autoencoder for lossless compression of quantum data
| Autor: | Chuan-Feng Li, Chang-Jiang Huang, Guang-Can Guo, Guo-Yong Xiang, Hailan Ma, Jun-Feng Tang, Chunlin Chen, Daoyi Dong, Qi Yin |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Physics
Lossless compression Computer Science::Machine Learning Quantum Physics FOS: Physical sciences Data_CODINGANDINFORMATIONTHEORY 01 natural sciences Autoencoder 010305 fluids & plasmas Statistics::Machine Learning Dimension (vector space) ComputerSystemsOrganization_MISCELLANEOUS 0103 physical sciences Linear independence Quantum information Quantum Physics (quant-ph) 010306 general physics Realization (systems) Algorithm Quantum Data compression |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|