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arXiv 2005.10555quant-ph
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基于推荐系统的量子进化的有效刻画

Efficient Characterization of Quantum Evolutions via a Recommender System

Priya Batra, Anukriti Singh, T. S. Mahesh

摘要:

我们展示了通过矩阵分解算法,一种特殊类型的推荐系统(RS)表征量子进化。经历量子演化的系统可以用几种方式来表征。在这里,我们选择(i)通过熵、负性或不和谐等度量量化的量子相关性,以及(ii)状态保真度。使用多达10个量子比特的量子寄存器,我们证明了RS可以有效地表征酉和非酉演化。在对两个量子比特中的RS进行了详细的性能分析之后,我们证明了它可以用来区分一个干净的量子相关数据库与一个嘈杂的或假的数据库。此外,我们发现,RS带来了一个显着的计算优势,建立一个大型数据库的量子不和谐,其中没有简单的封闭形式的表达存在。此外,RS可以有效地表征系统经历非幺正演化的量子不和谐减少以及状态保真度。最后,我们利用RS构造了一个非线性量子系统的不和谐相空间。

英文摘要:

We demonstrate characterizing quantum evolutions via matrix factorization algorithm, a particular type of the recommender system (RS). A system undergoing a quantum evolution can be characterized in several ways. Here we choose (i) quantum correlations quantified by measures such as entropy, negativity, or discord, and (ii) state-fidelity. Using quantum registers with up to 10 qubits, we demonstrate that an RS can efficiently characterize both unitary and nonunitary evolutions. After carrying out a detailed performance analysis of the RS in two qubits, we show that it can be used to distinguish a clean database of quantum correlations from a noisy or a fake one. Moreover, we find that the RS brings about a significant computational advantage for building a large database of quantum discord, for which no simple closed-form expression exists. Also, RS can efficiently characterize systems undergoing nonunitary evolutions in terms of quantum discord reduction as well as state-fidelity. Finally, we utilize RS for the construction of discord phase space in a nonlinear quantum system.

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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