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通过置于热库中的两量子比特系统生成类维尔纳态及其在解决二元分类问题中的应用。

Generation of Werner-like states via a two-qubit system plunged in a thermal reservoir and their application in solving binary classification problems.

作者信息

Ghasemian E, Tavassoly M K

机构信息

Optics and Laser Group, Faculty of Physics, Yazd University, Yazd, Iran.

出版信息

Sci Rep. 2021 Feb 11;11(1):3554. doi: 10.1038/s41598-021-82880-3.

DOI:10.1038/s41598-021-82880-3
PMID:33574398
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7878766/
Abstract

We present a theoretical scheme for the generation of stationary entangled states. To achieve the purpose we consider an open quantum system consisting of a two-qubit plunged in a thermal bath, as the source of dissipation, and then analytically solve the corresponding quantum master equation. We generate two classes of stationary entangled states including the Werner-like and maximally entangled mixed states. In this regard, since the solution of the system depends on its initial state, we can manipulate it and construct robust Bell-like state. In the continuation, we analytically obtain the population and coherence of the considered two-qubit system and show that the residual coherence can be maintained even in the equilibrium condition. Finally, we successfully encode our two-qubit system to solve a binary classification problem. We demonstrate that, the introduced classifiers present high accuracy without requiring any iterative method. In addition, we show that the quantum based classifiers beat the classical ones.

摘要

我们提出了一种生成稳态纠缠态的理论方案。为实现这一目的,我们考虑一个开放量子系统,它由置于热浴中的两比特组成,热浴作为耗散源,然后解析求解相应的量子主方程。我们生成了两类稳态纠缠态,包括类韦尔纳态和最大纠缠混合态。在这方面,由于系统的解取决于其初始状态,我们可以对其进行操控并构建鲁棒的类贝尔态。接着,我们解析得到了所考虑的两比特系统的布居数和相干性,并表明即使在平衡条件下,剩余相干性也能得以保持。最后,我们成功地对两比特系统进行编码以解决一个二元分类问题。我们证明,所引入的分类器具有高精度,无需任何迭代方法。此外,我们表明基于量子的分类器优于经典分类器。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/667488422f3b/41598_2021_82880_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/c0d41c8ca6ad/41598_2021_82880_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/f2cace27f743/41598_2021_82880_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/2ebd321a7816/41598_2021_82880_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/667488422f3b/41598_2021_82880_Fig4_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/c0d41c8ca6ad/41598_2021_82880_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/f2cace27f743/41598_2021_82880_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/2ebd321a7816/41598_2021_82880_Fig3_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/97c4/7878766/667488422f3b/41598_2021_82880_Fig4_HTML.jpg

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