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arXiv 2408.03071cond-mat.mes-hallcond-mat.supr-con
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从完美到不完美的“穷人的马约拉纳费米子”:最小Kitaev链中的实现

From perfect to imperfect poor man's Majoranas in minimal Kitaev chains

Melina Luethi, Henry F. Legg, Daniel Loss, Jelena Klinovaja

AI总结:

本文在有限塞曼能量且无库仑相互作用的系统中解析推导了最小Kitaev链中PMMs的甜点条件,并通过数值模型证明实际系统中只能近似满足该条件,从而得到高度局域但不完美局域的近零能态,揭示了PMMs分类的任意性。

摘要:

“穷人的马约拉纳费米子”(PMMs)有望在由超导体连接的量子点链构成的高度可调装置中工程化马约拉纳束缚态。由于近期在控制弹性共隧穿(ECT)和交叉安德烈耶夫反射(CAR)振幅方面取得进展,而这两者是实现PMMs的关键要素,因此对PMMs的实验研究引起了极大兴趣。此前,关于产生PMMs的“甜点”条件的解析研究主要集中于具有无限塞曼能量的系统。在此,我们在无库仑相互作用且塞曼能量有限的系统中解析推导出PMMs的甜点条件。随后,我们考虑了两个数值模型:一个模型中ECT和CAR通过超导体体态传输,另一个模型中它们通过安德烈耶夫束缚态传输。我们证明,在这些更现实的模型中,解析甜点条件只能被近似满足,而无法被精确满足。因此,在这些系统中我们并未发现完美的PMMs,而是发现了高度局域但并非完美局域的近零能态。这些态可被视为不完美的PMMs,其分类依赖于阈值,这给PMMs的概念增加了一定的任意性。

英文摘要:

Poor man's Majoranas (PMMs) hold the promise to engineer Majorana bound states in a highly tunable setup consisting of a chain of quantum dots that are connected via superconductors. Due to recent progress in controlling the amplitudes of elastic cotunneling (ECT) and crossed Andreev reflection (CAR), two vital ingredients for PMMs, experimental investigations of PMMs have gained significant interest. Previously, analytic conditions for the "sweet spots" that result in PMMs have focused on systems with infinite Zeeman energy. Here, we derive analytically a sweet spot condition for PMMs in a system with finite Zeeman energy in the absence of Coulomb interaction. We then consider two numerical models, one in which ECT and CAR are transmitted via superconducting bulk states and one in which they are transmitted via an Andreev bound state. We demonstrate that the analytical sweet spot conditions can only be approximated in these more realistic models, but they cannot be satisfied exactly. As a consequence, we do not find perfect PMMs in these systems, but instead near-zero-energy states that are highly, but not perfectly, localized. These states can be considered as imperfect PMMs and their classification relies on threshold values, which adds some arbitrariness to the concept of PMMs.

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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