首页    期刊浏览 2024年09月12日 星期四
登录注册

文章基本信息

  • 标题:Almost Optimal Classical Approximation Algorithms for a Quantum Generalization of Max-Cut
  • 本地全文:下载
  • 作者:Sevag Gharibian ; Ojas Parekh
  • 期刊名称:LIPIcs : Leibniz International Proceedings in Informatics
  • 电子版ISSN:1868-8969
  • 出版年度:2019
  • 卷号:145
  • 页码:1-17
  • DOI:10.4230/LIPIcs.APPROX-RANDOM.2019.31
  • 出版社:Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
  • 摘要:Approximation algorithms for constraint satisfaction problems (CSPs) are a central direction of study in theoretical computer science. In this work, we study classical product state approximation algorithms for a physically motivated quantum generalization of Max-Cut, known as the quantum Heisenberg model. This model is notoriously difficult to solve exactly, even on bipartite graphs, in stark contrast to the classical setting of Max-Cut. Here we show, for any interaction graph, how to classically and efficiently obtain approximation ratios 0.649 (anti-feromagnetic XY model) and 0.498 (anti-ferromagnetic Heisenberg XYZ model). These are almost optimal; we show that the best possible ratios achievable by a product state for these models is 2/3 and 1/2, respectively.
  • 关键词:Approximation algorithm; Max-Cut; local Hamiltonian; QMA-hard; Heisenberg model; product state
国家哲学社会科学文献中心版权所有