4.6 Article

Mutually unbiased maximally entangled bases from difference matrices

出版社

IOP Publishing Ltd
DOI: 10.1088/1751-8121/ac9200

关键词

mutually unbiased bases; maximally entangled states; difference matrices; Latin squares

资金

  1. Beijing Postdoctoral Research Foundation [2022ZZ071]
  2. Natural Science Foundation of Hebei Province [F2021205001]
  3. NSFC [11871019, 12075159, 12171044, 62272208]
  4. Beijing Natural Science Foundation [Z190005]
  5. Academy for Multidisciplinary Studies, Capital Normal University
  6. Academician Innovation Platform of Hainan Province
  7. Shenzhen Institute for Quantum Science and Engineering, Southern University of Science and Technology [SIQSE202001]

向作者/读者索取更多资源

Based on maximally entangled states, this paper explores the constructions of mutually unbiased bases in bipartite quantum systems. A new way to construct mutually unbiased bases using difference matrices in the theory of combinatorial designs is presented. Various constructions of mutually unbiased bases are established, including cases involving prime power q and composite numbers of non-prime power d.
Based on maximally entangled states, we explore the constructions of mutually unbiased bases in bipartite quantum systems. We present a new way to construct mutually unbiased bases by difference matrices in the theory of combinatorial designs. In particular, we establish q mutually unbiased bases with q - 1 maximally entangled bases and one product basis in C-q circle times C-q for arbitrary prime power q. In addition, we construct maximally entangled bases for dimension of composite numbers of non-prime power, such as five maximally entangled bases in C-12 circle times C-12 and C-21 circle times C-21, which improve the known lower bounds for d = 3m, with (3, m) = 1 in C-d circle times C-d. Furthermore, we construct p + 1 mutually unbiased bases with p maximally entangled bases and one product basis in C-p circle times C-p2 for arbitrary prime number p.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.6
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据