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arXiv 1805.09784quant-ph
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具有初始叠加态的量子行走的实验演示

Experimental demonstration of quantum walks with initial superposition states

Qi-Ping Su, Yu Zhang, Li Yu, Jia-Qi Zhou, Jin-Shuang Jin, Xiao-Qiang Xu, Shao-Jie Xiong, QingJun Xu, Zhe Sun, Kefei Chen, Franco Nori, Chui-Ping Yang

摘要:

离散时间量子行走初始叠加态的制备是离散时间量子行走研究和应用的必要条件。在线性光学中,制备硬币的初始叠加态很容易,它们总是由偏振态编码;而制备步行者的叠加态是具有挑战性的。基于一种新的编码方法,我们在这里提出了一个DTQW协议,在线性光学,使步行者和硬币的任意初始叠加态的准备。有了这个协议,我们报告的DTQW的实验演示与步行者最初在叠加态,仅使用无源线性光学元件。实验研究了步行者不同初始叠加态对DTQW传播速度以及硬币与步行者纠缠度的影响。当步行者从叠加态出发时,我们证明了DTQW的性质与从单个位置出发的DTQW的性质有很大的不同。这些结果揭示了DTQW的不同性质,为研究任意初始态的DTQW奠定了基础。此外,该编码方法使得能够使用单个物理量子比特对任意高维量子态进行编码,并且可以用于实现其他量子信息任务。

英文摘要:

The preparation of initial superposition states of discrete-time quantum walks (DTQWs) are necessary for the study and applications of DTQWs. In linear optics, it is easy to prepare initial superposition states of the coin, which are always encoded by polarization states; while the preparation of superposition states of the walker is challenging. Based on a novel encoding method, we here propose a DTQW protocol in linear optics which enables the preparation of arbitrary initial superposition states of the walker and the coin. With this protocol, we report an experimental demonstration of DTQW with the walker initially in superposition states, by using only passive linear-optical elements. The effects of the walker's different initial superposition states on the spread speed of the DTQW and on the entanglement between the coin and the walker are also experimentally investigated, which have not been reported before. When the walker starts with superposition states, we show that the properties of DTQW are very different from those of DTQW starting with a single position. Our findings reveal different properties of DTQW and paves an avenue to study DTQW with arbitrary initial states. Moreover, the encoding method enables one to encode an arbitrary high-dimensional quantum state using a single physical qubit and may be adopted to implement other quantum information tasks.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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