首页> 美国卫生研究院文献>Scientific Reports >Automated generation of Kochen-Specker sets
【2h】

Automated generation of Kochen-Specker sets

机译:自动生成Kochen-Specker集

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务,本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文,但由于OA文献来源多样且变更频繁,仍可能出现获取不到、文献不完整或与标题不符等情况,如果获取不到我们将提供退款服务。请知悉。
获取外文期刊封面目录资料

摘要

Quantum contextuality turns out to be a necessary resource for universal quantum computation and also has applications in quantum communication. Thus it becomes important to generate contextual sets of arbitrary structure and complexity to enable a variety of implementations. In recent years, such generation has been done for contextual sets known as Kochen-Specker sets. Up to now, two approaches have been used for massive generation of non-isomorphic Kochen-Specker sets: exhaustive generation up to a given size and downward generation from master sets and their associated coordinatizations. Master sets were obtained earlier from serendipitous or intuitive connections with polytopes or Pauli operators, and more recently from arbitrary vector components using an algorithm that generates orthogonal vector groupings from them. However, both upward and downward generation face an inherent exponential complexity barrier. In contrast, in this paper we present methods and algorithms that we apply to downward generation that can overcome the exponential barrier in many cases of interest. These involve tailoring and manipulating Kochen-Specker master sets obtained from a small number of simple vector components, filtered by the features of the sets we aim to obtain. Some of the classes of Kochen-Specker sets we generate contain all previously known ones, and others are completely novel. We provide examples of both kinds in 4- and 6-dim Hilbert spaces. We also give a brief introduction for a wider audience and a novice reader.
机译:事实证明,量子上下文是通用量子计算的必要资源,并且在量子通信中也有应用。因此,生成任意结构和复杂性的上下文集以实现各种实现就变得很重要。近年来,已经针对称为Kochen-Specker集的上下文集进行了这种生成。迄今为止,已经使用了两种方法来大量生成非同构的Kochen-Specker集:从穷集生成到给定大小,然后从主集及其相关的协调向下生成。可以从与多面体或Pauli运算符的偶然或直观连接中获得主集,最近可以使用从其生成正交矢量分组的算法从任意矢量分量中获取主集。但是,向上和向下生成都面临固有的指数复杂性障碍。相反,在本文中,我们介绍了适用于向下生成的方法和算法,这些方法和算法可以克服许多感兴趣的情况下的指数障碍。这些涉及裁剪和处理从少量简单向量分量中获得的Kochen-Specker主集合,并通过我们旨在获得的集合特征进行过滤。我们生成的某些Kochen-Specker集类别包含所有先前已知的类别,而另一些则是完全新颖的。我们提供了4维和6维希尔伯特空间中的两种示例。我们还将为广大读者和新手做一个简短的介绍。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号