Interactive Proofs for Quantum Computations
Total Page:16
File Type:pdf, Size:1020Kb
Load more
										Recommended publications
									
								- 
												
												Nitin Saurabh the Institute of Mathematical Sciences, Chennai
ALGEBRAIC MODELS OF COMPUTATION By Nitin Saurabh The Institute of Mathematical Sciences, Chennai. A thesis submitted to the Board of Studies in Mathematical Sciences In partial fulllment of the requirements For the Degree of Master of Science of HOMI BHABHA NATIONAL INSTITUTE April 2012 CERTIFICATE Certied that the work contained in the thesis entitled Algebraic models of Computation, by Nitin Saurabh, has been carried out under my supervision and that this work has not been submitted elsewhere for a degree. Meena Mahajan Theoretical Computer Science Group The Institute of Mathematical Sciences, Chennai ACKNOWLEDGEMENTS I would like to thank my advisor Prof. Meena Mahajan for her invaluable guidance and continuous support since my undergraduate days. Her expertise and ideas helped me comprehend new techniques. Her guidance during the preparation of this thesis has been invaluable. I also thank her for always being there to discuss and clarify any matter. I am extremely grateful to all the faculty members of theory group at IMSc and CMI for their continuous encouragement and giving me an opportunity to learn from them. I would like to thank all my friends, at IMSc and CMI, for making my stay in Chennai a memorable one. Most of all, I take this opportunity to thank my parents, my uncle and my brother. Abstract Valiant [Val79, Val82] had proposed an analogue of the theory of NP-completeness in an entirely algebraic framework to study the complexity of polynomial families. Artihmetic circuits form the most standard model for studying the complexity of polynomial computations. In a note [Val92], Valiant argued that in order to prove lower bounds for boolean circuits, obtaining lower bounds for arithmetic circuits should be a rst step. - 
												
												Week 1: an Overview of Circuit Complexity 1 Welcome 2
Topics in Circuit Complexity (CS354, Fall’11) Week 1: An Overview of Circuit Complexity Lecture Notes for 9/27 and 9/29 Ryan Williams 1 Welcome The area of circuit complexity has a long history, starting in the 1940’s. It is full of open problems and frontiers that seem insurmountable, yet the literature on circuit complexity is fairly large. There is much that we do know, although it is scattered across several textbooks and academic papers. I think now is a good time to look again at circuit complexity with fresh eyes, and try to see what can be done. 2 Preliminaries An n-bit Boolean function has domain f0; 1gn and co-domain f0; 1g. At a high level, the basic question asked in circuit complexity is: given a collection of “simple functions” and a target Boolean function f, how efficiently can f be computed (on all inputs) using the simple functions? Of course, efficiency can be measured in many ways. The most natural measure is that of the “size” of computation: how many copies of these simple functions are necessary to compute f? Let B be a set of Boolean functions, which we call a basis set. The fan-in of a function g 2 B is the number of inputs that g takes. (Typical choices are fan-in 2, or unbounded fan-in, meaning that g can take any number of inputs.) We define a circuit C with n inputs and size s over a basis B, as follows. C consists of a directed acyclic graph (DAG) of s + n + 2 nodes, with n sources and one sink (the sth node in some fixed topological order on the nodes). - 
												
												The Complexity Zoo
The Complexity Zoo Scott Aaronson www.ScottAaronson.com LATEX Translation by Chris Bourke [email protected] 417 classes and counting 1 Contents 1 About This Document 3 2 Introductory Essay 4 2.1 Recommended Further Reading ......................... 4 2.2 Other Theory Compendia ............................ 5 2.3 Errors? ....................................... 5 3 Pronunciation Guide 6 4 Complexity Classes 10 5 Special Zoo Exhibit: Classes of Quantum States and Probability Distribu- tions 110 6 Acknowledgements 116 7 Bibliography 117 2 1 About This Document What is this? Well its a PDF version of the website www.ComplexityZoo.com typeset in LATEX using the complexity package. Well, what’s that? The original Complexity Zoo is a website created by Scott Aaronson which contains a (more or less) comprehensive list of Complexity Classes studied in the area of theoretical computer science known as Computa- tional Complexity. I took on the (mostly painless, thank god for regular expressions) task of translating the Zoo’s HTML code to LATEX for two reasons. First, as a regular Zoo patron, I thought, “what better way to honor such an endeavor than to spruce up the cages a bit and typeset them all in beautiful LATEX.” Second, I thought it would be a perfect project to develop complexity, a LATEX pack- age I’ve created that defines commands to typeset (almost) all of the complexity classes you’ll find here (along with some handy options that allow you to conveniently change the fonts with a single option parameters). To get the package, visit my own home page at http://www.cse.unl.edu/~cbourke/. - 
												
												UNIONS of LINES in Fn 1. Introduction the Main Problem We
UNIONS OF LINES IN F n RICHARD OBERLIN Abstract. We show that if a collection of lines in a vector space over a finite field has \dimension" at least 2(d−1)+β; then its union has \dimension" at least d + β: This is the sharp estimate of its type when no structural assumptions are placed on the collection of lines. We also consider some refinements and extensions of the main result, including estimates for unions of k-planes. 1. Introduction The main problem we will consider here is to give a lower bound for the dimension of the union of a collection of lines in terms of the dimension of the collection of lines, without imposing a structural hy- pothesis on the collection (in contrast to the Kakeya problem where one assumes that the lines are direction-separated, or perhaps satisfy the weaker \Wolff axiom"). Specifically, we are motivated by the following conjecture of D. Ober- lin (hdim denotes Hausdorff dimension). Conjecture 1.1. Suppose d ≥ 1 is an integer, that 0 ≤ β ≤ 1; and that L is a collection of lines in Rn with hdim(L) ≥ 2(d − 1) + β: Then [ (1) hdim( L) ≥ d + β: L2L The bound (1), if true, would be sharp, as one can see by taking L to be the set of lines contained in the d-planes belonging to a β- dimensional family of d-planes. (Furthermore, there is nothing to be gained by taking 1 < β ≤ 2 since the dimension of the set of lines contained in a d + 1-plane is 2(d − 1) + 2.) Standard Fourier-analytic methods show that (1) holds for d = 1, but the conjecture is open for d > 1: As a model problem, one may consider an analogous question where Rn is replaced by a vector space over a finite-field. - 
												
												Collapsing Exact Arithmetic Hierarchies
Electronic Colloquium on Computational Complexity, Report No. 131 (2013) Collapsing Exact Arithmetic Hierarchies Nikhil Balaji and Samir Datta Chennai Mathematical Institute fnikhil,[email protected] Abstract. We provide a uniform framework for proving the collapse of the hierarchy, NC1(C) for an exact arith- metic class C of polynomial degree. These hierarchies collapses all the way down to the third level of the AC0- 0 hierarchy, AC3(C). Our main collapsing exhibits are the classes 1 1 C 2 fC=NC ; C=L; C=SAC ; C=Pg: 1 1 NC (C=L) and NC (C=P) are already known to collapse [1,18,19]. We reiterate that our contribution is a framework that works for all these hierarchies. Our proof generalizes a proof 0 1 from [8] where it is used to prove the collapse of the AC (C=NC ) hierarchy. It is essentially based on a polynomial degree characterization of each of the base classes. 1 Introduction Collapsing hierarchies has been an important activity for structural complexity theorists through the years [12,21,14,23,18,17,4,11]. We provide a uniform framework for proving the collapse of the NC1 hierarchy over an exact arithmetic class. Using 0 our method, such a hierarchy collapses all the way down to the AC3 closure of the class. 1 1 1 Our main collapsing exhibits are the NC hierarchies over the classes C=NC , C=L, C=SAC , C=P. Two of these 1 1 hierarchies, viz. NC (C=L); NC (C=P), are already known to collapse ([1,19,18]) while a weaker collapse is known 0 1 for a third one viz. - 
												
												Dspace 1.8 Documentation
DSpace 1.8 Documentation DSpace 1.8 Documentation Author: The DSpace Developer Team Date: 03 November 2011 URL: https://wiki.duraspace.org/display/DSDOC18 Page 1 of 621 DSpace 1.8 Documentation Table of Contents 1 Preface _____________________________________________________________________________ 13 1.1 Release Notes ____________________________________________________________________ 13 2 Introduction __________________________________________________________________________ 15 3 Functional Overview ___________________________________________________________________ 17 3.1 Data Model ______________________________________________________________________ 17 3.2 Plugin Manager ___________________________________________________________________ 19 3.3 Metadata ________________________________________________________________________ 19 3.4 Packager Plugins _________________________________________________________________ 20 3.5 Crosswalk Plugins _________________________________________________________________ 21 3.6 E-People and Groups ______________________________________________________________ 21 3.6.1 E-Person __________________________________________________________________ 21 3.6.2 Groups ____________________________________________________________________ 22 3.7 Authentication ____________________________________________________________________ 22 3.8 Authorization _____________________________________________________________________ 22 3.9 Ingest Process and Workflow ________________________________________________________ 24 - 
												
												Glossary of Complexity Classes
App endix A Glossary of Complexity Classes Summary This glossary includes selfcontained denitions of most complexity classes mentioned in the b o ok Needless to say the glossary oers a very minimal discussion of these classes and the reader is re ferred to the main text for further discussion The items are organized by topics rather than by alphab etic order Sp ecically the glossary is partitioned into two parts dealing separately with complexity classes that are dened in terms of algorithms and their resources ie time and space complexity of Turing machines and complexity classes de ned in terms of nonuniform circuits and referring to their size and depth The algorithmic classes include timecomplexity based classes such as P NP coNP BPP RP coRP PH E EXP and NEXP and the space complexity classes L NL RL and P S P AC E The non k uniform classes include the circuit classes P p oly as well as NC and k AC Denitions and basic results regarding many other complexity classes are available at the constantly evolving Complexity Zoo A Preliminaries Complexity classes are sets of computational problems where each class contains problems that can b e solved with sp ecic computational resources To dene a complexity class one sp ecies a mo del of computation a complexity measure like time or space which is always measured as a function of the input length and a b ound on the complexity of problems in the class We follow the tradition of fo cusing on decision problems but refer to these problems using the terminology of promise problems - 
												
												Interactive Oracle Proofs with Constant Rate and Query Complexity∗†
Interactive Oracle Proofs with Constant Rate and Query Complexity∗† Eli Ben-Sasson1, Alessandro Chiesa2, Ariel Gabizon‡3, Michael Riabzev4, and Nicholas Spooner5 1 Technion, Haifa, Israel [email protected] 2 University of California, Berkeley, CA, USA [email protected] 3 Zerocoin Electronic Coin Company (Zcash), Boulder, CO, USA [email protected] 4 Technion, Haifa, Israel [email protected] 5 University of Toronto, Toronto, Canada [email protected] Abstract We study interactive oracle proofs (IOPs) [7, 43], which combine aspects of probabilistically check- able proofs (PCPs) and interactive proofs (IPs). We present IOP constructions and techniques that let us achieve tradeoffs in proof length versus query complexity that are not known to be achievable via PCPs or IPs alone. Our main results are: 1. Circuit satisfiability has 3-round IOPs with linear proof length (counted in bits) and constant query complexity. 2. Reed–Solomon codes have 2-round IOPs of proximity with linear proof length and constant query complexity. 3. Tensor product codes have 1-round IOPs of proximity with sublinear proof length and constant query complexity. (A familiar example of a tensor product code is the Reed–Muller code with a bound on individual degrees.) For all the above, known PCP constructions give quasilinear proof length and constant query complexity [12, 16]. Also, for circuit satisfiability, [10] obtain PCPs with linear proof length but sublinear (and super-constant) query complexity. As in [10], we rely on algebraic-geometry codes to obtain our first result; but, unlike that work, our use of such codes is much “lighter” because we do not rely on any automorphisms of the code. - 
												
												User's Guide for Complexity: a LATEX Package, Version 0.80
User’s Guide for complexity: a LATEX package, Version 0.80 Chris Bourke April 12, 2007 Contents 1 Introduction 2 1.1 What is complexity? ......................... 2 1.2 Why a complexity package? ..................... 2 2 Installation 2 3 Package Options 3 3.1 Mode Options .............................. 3 3.2 Font Options .............................. 4 3.2.1 The small Option ....................... 4 4 Using the Package 6 4.1 Overridden Commands ......................... 6 4.2 Special Commands ........................... 6 4.3 Function Commands .......................... 6 4.4 Language Commands .......................... 7 4.5 Complete List of Class Commands .................. 8 5 Customization 15 5.1 Class Commands ............................ 15 1 5.2 Language Commands .......................... 16 5.3 Function Commands .......................... 17 6 Extended Example 17 7 Feedback 18 7.1 Acknowledgements ........................... 19 1 Introduction 1.1 What is complexity? complexity is a LATEX package that typesets computational complexity classes such as P (deterministic polynomial time) and NP (nondeterministic polynomial time) as well as sets (languages) such as SAT (satisfiability). In all, over 350 commands are defined for helping you to typeset Computational Complexity con- structs. 1.2 Why a complexity package? A better question is why not? Complexity theory is a more recent, though mature area of Theoretical Computer Science. Each researcher seems to have his or her own preferences as to how to typeset Complexity Classes and has built up their own personal LATEX commands file. This can be frustrating, to say the least, when it comes to collaborations or when one has to go through an entire series of files changing commands for compatibility or to get exactly the look they want (or what may be required). - 
												
												A Short History of Computational Complexity
The Computational Complexity Column by Lance FORTNOW NEC Laboratories America 4 Independence Way, Princeton, NJ 08540, USA [email protected] http://www.neci.nj.nec.com/homepages/fortnow/beatcs Every third year the Conference on Computational Complexity is held in Europe and this summer the University of Aarhus (Denmark) will host the meeting July 7-10. More details at the conference web page http://www.computationalcomplexity.org This month we present a historical view of computational complexity written by Steve Homer and myself. This is a preliminary version of a chapter to be included in an upcoming North-Holland Handbook of the History of Mathematical Logic edited by Dirk van Dalen, John Dawson and Aki Kanamori. A Short History of Computational Complexity Lance Fortnow1 Steve Homer2 NEC Research Institute Computer Science Department 4 Independence Way Boston University Princeton, NJ 08540 111 Cummington Street Boston, MA 02215 1 Introduction It all started with a machine. In 1936, Turing developed his theoretical com- putational model. He based his model on how he perceived mathematicians think. As digital computers were developed in the 40's and 50's, the Turing machine proved itself as the right theoretical model for computation. Quickly though we discovered that the basic Turing machine model fails to account for the amount of time or memory needed by a computer, a critical issue today but even more so in those early days of computing. The key idea to measure time and space as a function of the length of the input came in the early 1960's by Hartmanis and Stearns. - 
												
												The Computational Complexity of Probabilistic Planning
Journal of Arti cial Intelligence Research 9 1998 1{36 Submitted 1/98; published 8/98 The Computational Complexity of Probabilistic Planning Michael L. Littman [email protected] Department of Computer Science, Duke University Durham, NC 27708-0129 USA Judy Goldsmith [email protected] Department of Computer Science, University of Kentucky Lexington, KY 40506-0046 USA Martin Mundhenk [email protected] FB4 - Theoretische Informatik, Universitat Trier D-54286 Trier, GERMANY Abstract We examine the computational complexity of testing and nding small plans in proba- bilistic planning domains with b oth at and prop ositional representations. The complexity of plan evaluation and existence varies with the plan typ e sought; we examine totally ordered plans, acyclic plans, and lo oping plans, and partially ordered plans under three natural de nitions of plan value. We show that problems of interest are complete for a PP PP variety of complexity classes: PL, P, NP, co-NP, PP,NP , co-NP , and PSPACE. In PP the pro cess of proving that certain planning problems are complete for NP ,weintro duce PP a new basic NP -complete problem, E-Majsat, which generalizes the standard Bo olean satis ability problem to computations involving probabilistic quantities; our results suggest that the development of go o d heuristics for E-Majsat could b e imp ortant for the creation of ecient algorithms for a wide variety of problems. 1. Intro duction Recent work in arti cial-intelligence planning has addressed the problem of nding e ec- tive plans in domains in which op erators have probabilistic e ects Drummond & Bresina, 1990; Mansell, 1993; Drap er, Hanks, & Weld, 1994; Ko enig & Simmons, 1994; Goldman & Bo ddy, 1994; Kushmerick, Hanks, & Weld, 1995; Boutilier, Dearden, & Goldszmidt, 1995; Dearden & Boutilier, 1997; Kaelbling, Littman, & Cassandra, 1998; Boutilier, Dean, & Hanks, 1998. - 
												
												TC Circuits for Algorithmic Problems in Nilpotent Groups
TC0 Circuits for Algorithmic Problems in Nilpotent Groups Alexei Myasnikov1 and Armin Weiß2 1 Stevens Institute of Technology, Hoboken, NJ, USA 2 Universität Stuttgart, Germany Abstract Recently, Macdonald et. al. showed that many algorithmic problems for finitely generated nilpo- tent groups including computation of normal forms, the subgroup membership problem, the con- jugacy problem, and computation of subgroup presentations can be done in LOGSPACE. Here we follow their approach and show that all these problems are complete for the uniform circuit class TC0 – uniformly for all r-generated nilpotent groups of class at most c for fixed r and c. Moreover, if we allow a certain binary representation of the inputs, then the word problem and computation of normal forms is still in uniform TC0, while all the other problems we examine are shown to be TC0-Turing reducible to the problem of computing greatest common divisors and expressing them as linear combinations. 1998 ACM Subject Classification F.2.2 Nonnumerical Algorithms and Problems, G.2.0 Discrete Mathematics Keywords and phrases nilpotent groups, TC0, abelian groups, word problem, conjugacy problem, subgroup membership problem, greatest common divisors Digital Object Identifier 10.4230/LIPIcs.MFCS.2017.23 1 Introduction The word problem (given a word over the generators, does it represent the identity?) is one of the fundamental algorithmic problems in group theory introduced by Dehn in 1911 [3]. While for general finitely presented groups all these problems are undecidable [22, 2], for many particular classes of groups decidability results have been established – not just for the word problem but also for a wide range of other problems.