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Application of SDP to product rules and quantum query complexity .

机译:SDP在产品规则和量子查询复杂度中的应用。

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摘要

In recent years, semidefinite programming has played a vital role in shaping complexity theory and quantum computing. There have been numerous applications ranging from estimating quantum values, over approximating combinatorial quantities, to proving various bounds. This work extends the use of semidefinite programs (SDPs) to proving product rules and to characterizing quantum query complexity.;In the first application, we provide a general framework to establishing product rules for quantities that can be expressed (or approximated) using SDPs. We use duality theory to give product rules, which bound the value of the "product" of two problems in terms of their value. Some previous results have implicitly used the properties of SDPs to give such product rules. Here we give sufficient and necessary conditions under which these approaches work, thereby enabling us to capture these previous results under our unified framework. We also include a discussion about alternate definitions of what a "product" means and how they fit into our approach.;The second application provides an SDP characterization of quantum query complexity, which is one of the ways in which complexity of a function can be measured. It is known that quantum query complexity can be lower bounded by the so-called "adversary method" which is expressible as a semidefinite program. Recently, Ben Reichardt showed that the adversary method leads to a tight lower bound for boolean functions by converting the solution of this SDP (of adversary method) into an algorithm. We show that a related SDP, called "witness size" in this thesis, provides a tight bound on the quantum query complexity of non boolean functions (total as well as partial). This witness size SDP is also used to give composition results for quantum query complexity. We also show that the witness size is bounded by a constant multiple of the adversary bound.;Finally, we briefly explore whether other convex programming paradigms can be useful in complexity theory. One of them is copositive programming. We show that one of the recent result about parallel repetition of unique games, by Barak et.al., can be interpreted as an application of copositive programming.
机译:近年来,半定程序设计在塑造复杂性理论和量子计算方面发挥了至关重要的作用。从估计量子值,近似组合量到证明各种界限,都有许多应用。这项工作将半定程序(SDP)的使用扩展到证明乘积规则并表征量子查询的复杂性。在第一个应用程序中,我们提供了一个通用框架来建立可使用SDP表示(或近似)的数量的乘积规则。我们使用对偶理论来给出乘积规则,该乘积规则就两个问题的“乘积”的值而言,限制了它们的价值。某些先前的结果隐式地使用了SDP的属性来给出此类产品规则。在这里,我们给出了这些方法起作用的充分和必要条件,从而使我们能够在统一框架下获得以前的结果。我们还将就“产品”的含义以及它们如何适合我们的方法的替代定义进行讨论。;第二个应用程序提供了量子查询复杂度的SDP表征,这是可以实现函数复杂度的一种方式测量。众所周知,量子查询的复杂性可以通过所谓的“对手方法”来降低,该方法可以表示为半定程序。最近,本·里查特(Ben Reichardt)表明,通过将SDP(对手方法)的解决方案转换为算法,对手方法导致布尔函数的紧迫下界。我们显示了一个相关的SDP,在本文中称为“见证大小”,为非布尔函数(全部和部分)的量子查询复杂度提供了严格的界限。该见证人大小的SDP还用于给出量子查询复杂性的合成结果。我们还证明了证人的规模受对手界限的恒定倍数的限制。最后,我们简要地探讨了其他凸编程范例在复杂性理论中是否有用。其中之一是协同编程。我们证明了Barak等人关于并行重复独特游戏的最新结果之一,可以解释为一种协同编程的应用。

著录项

  • 作者

    Mittal, Rajat.;

  • 作者单位

    Rutgers The State University of New Jersey - New Brunswick.;

  • 授予单位 Rutgers The State University of New Jersey - New Brunswick.;
  • 学科 Computer Science.
  • 学位 Ph.D.
  • 年度 2011
  • 页码 97 p.
  • 总页数 97
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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