Hailan Ma, Gary J. Mooney, Ian R. Petersen, Lloyd C. L. Hollenberg, Daoyi Dong
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引用次数: 0
Abstract
One of the fundamental tasks in quantum information theory is quantum data compression, which can be realized via quantum autoencoders that first compress quantum states to low-dimensional ones and then recover to the original ones with a reference state. When taking a pure reference state, there exists an upper bound for the encoding fidelity, which limits the compression of states with high entropy. To overcome the entropy inconsistency, we allow the reference state to be a mixed state and propose a cost function that combines the encoding fidelity and the quantum mutual information. We consider the reference states to be a mixture of maximally mixed states and pure states and propose three strategies for setting the ratio of mixedness. Numerical simulations of different states and experimental implementations on IBM quantum computers illustrate the effectiveness of our approach.
量子信息理论的基本任务之一是量子数据压缩,这可以通过量子自编码器来实现。量子自编码器首先将量子态压缩到低维量子态,然后通过参考态恢复到原始量子态。当采用纯参考态时,编码保真度存在上限,这限制了对高熵态的压缩。为了克服熵的不一致性,我们允许参考态为混合态,并提出了一种结合编码保真度和量子互信息的代价函数。我们认为参考态是最大混合态和纯态的混合物,并提出了三种设定混合比例的策略。对不同状态的数值模拟和在 IBM 量子计算机上的实验实现说明了我们方法的有效性。
期刊介绍:
The scope of npj Quantum Information spans across all relevant disciplines, fields, approaches and levels and so considers outstanding work ranging from fundamental research to applications and technologies.