The Strong Exponential Hierarchy Collapses*
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On Physical Problems That Are Slightly More Difficult Than
On physical problems that are slightly more difficult than QMA Andris Ambainis University of Latvia and IAS, Princeton Email: [email protected] Abstract We study the complexity of computational problems from quantum physics. Typi- cally, they are studied using the complexity class QMA (quantum counterpart of NP ) but some natural computational problems appear to be slightly harder than QMA. We introduce new complexity classes consisting of problems that are solvable with a small number of queries to a QMA oracle and use these complexity classes to quantify the complexity of several natural computational problems (for example, the complexity of estimating the spectral gap of a Hamiltonian). 1 Introduction Quantum Hamiltonian complexity [30] is a new field that combines quantum physics with computer science, by using the notions from computational complexity to study the com- plexity of problems that appear in quantum physics. One of central notions of Hamiltonian complexity is the complexity class QMA [25, 24, 40, 21, 3] which is the quantum counterpart of NP. QMA consists of all computational problems whose solutions can be verified in polynomial time on a quantum computer, given a quantum witness (a quantum state on a polynomial number of qubits). QMA captures the complexity of several interesting physical problems. For example, estimating the ground state energy of a physical system (described by a Hamiltonian) is arXiv:1312.4758v2 [quant-ph] 10 Apr 2014 a very important task in quantum physics. We can characterize the complexity of this problem by showing that it is QMA-complete, even if we restrict it to natural classes of Hamiltonians. -
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/. -
Boolean Hierarchies
Bo olean Hierarchies On Collapse Prop erties and Query Order Dissertation zur Erlangung des akademischen Grades do ctor rerum naturalium Dr rer nat vorgelegt dem Rat der Fakultat fur Mathematik und Informatik der FriedrichSchillerUniversitat Jena von DiplomMathematiker Harald Hemp el geb oren am August in Jena Gutachter Prof Dr Gerd Wechsung Prof Edith Hemaspaandra Prof Dr Klaus Wagner Tag des Rigorosums Tag der oentlichen Verteidigung To my family Acknowledgements Words can not express my deep gratitude to my advisor Professor Gerd Wechsung Gen erously he oered supp ort guidance and encouragement throughout the past four years Learning from him and working with him was and still is a pleasure and privilege I much ap preciate Through all the ups and downs of my research his optimism and humane warmth have made the downs less frustrating and the ups more encouraging I want to express my deep gratitude to Professor Lane Hemaspaandra and Professor Edith Hemaspaandra Allowing me to become part of so many joint pro jects has been a wonderful learning exp erience and I much b eneted from their scien tic exp ertise Their generous help and advice help ed me to gain insights into how research is done and made this thesis p ossible For serving as referees for this thesis I am grateful to Professor Edith Hemaspaandra and Professor Klaus Wagner Iwant to thank all my colleagues at Jena esp ecially HaikoMuller Dieter Kratsch Jorg Rothe Johannes Waldmann and Maren Hinrichs for generously oering help and supp ort A regarding the many little things -
The Polynomial Hierarchy
ij 'I '""T', :J[_ ';(" THE POLYNOMIAL HIERARCHY Although the complexity classes we shall study now are in one sense byproducts of our definition of NP, they have a remarkable life of their own. 17.1 OPTIMIZATION PROBLEMS Optimization problems have not been classified in a satisfactory way within the theory of P and NP; it is these problems that motivate the immediate extensions of this theory beyond NP. Let us take the traveling salesman problem as our working example. In the problem TSP we are given the distance matrix of a set of cities; we want to find the shortest tour of the cities. We have studied the complexity of the TSP within the framework of P and NP only indirectly: We defined the decision version TSP (D), and proved it NP-complete (corollary to Theorem 9.7). For the purpose of understanding better the complexity of the traveling salesman problem, we now introduce two more variants. EXACT TSP: Given a distance matrix and an integer B, is the length of the shortest tour equal to B? Also, TSP COST: Given a distance matrix, compute the length of the shortest tour. The four variants can be ordered in "increasing complexity" as follows: TSP (D); EXACTTSP; TSP COST; TSP. Each problem in this progression can be reduced to the next. For the last three problems this is trivial; for the first two one has to notice that the reduction in 411 j ;1 17.1 Optimization Problems 413 I 412 Chapter 17: THE POLYNOMIALHIERARCHY the corollary to Theorem 9.7 proving that TSP (D) is NP-complete can be used with DP. -
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. -
On the Power of Parity Polynomial Time Jin-Yi Cai1 Lane A
On the Power of Parity Polynomial Time Jin-yi Cai1 Lane A. Hemachandra2 December. 1987 CUCS-274-87 I Department of Computer Science. Yale University, New Haven. CT 06520 1Department of Computer Science. Columbia University. New York. NY 10027 On the Power of Parity Polynomial Time Jin-yi Gaia Lane A. Hemachandrat Department of Computer Science Department of Computer Science Yale University Columbia University New Haven, CT 06520 New York, NY 10027 December, 1987 Abstract This paper proves that the complexity class Ef)P, parity polynomial time [PZ83], contains the class of languages accepted by NP machines with few ac cepting paths. Indeed, Ef)P contains a. broad class of languages accepted by path-restricted nondeterministic machines. In particular, Ef)P contains the polynomial accepting path versions of NP, of the counting hierarchy, and of ModmNP for m > 1. We further prove that the class of nondeterministic path-restricted languages is closed under bounded truth-table reductions. 1 Introduction and Overview One of the goals of computational complexity theory is to classify the inclusions and separations of complexity classes. Though nontrivial separation of complexity classes is often a challenging problem (e.g. P i: NP?), the inclusion structure of complexity classes is progressively becoming clearer [Sim77,Lau83,Zac86,Sch87]. This paper proves that Ef)P contains a broad range of complexity classes. 1.1 Parity Polynomial Time The class Ef)P, parity polynomial time, was defined and studied by Papadimitriou and . Zachos as a "moderate version" of Valiant's counting class #P . • Research supported by NSF grant CCR-8709818. t Research supported in part by a Hewlett-Packard Corporation equipment grant. -
Completeness in the Boolean Hierarchy: Exact-Four-Colorability, Minimal Graph Uncolorability, and Exact Domatic Number Problems – a Survey
Journal of Universal Computer Science, vol. 12, no. 5 (2006), 551-578 submitted: 9/2/06, accepted: 23/5/06, appeared: 28/5/06 © J.UCS Completeness in the Boolean Hierarchy: Exact-Four-Colorability, Minimal Graph Uncolorability, and Exact Domatic Number Problems – a Survey Tobias Riege and J¨org Rothe (Institut f¨ur Informatik, Heinrich-Heine-Universit¨at D¨usseldorf, Germany {riege, rothe}@cs.uni-duesseldorf.de) Abstract: This paper surveys some of the work that was inspired by Wagner’s general technique to prove completeness in the levels of the boolean hierarchy over NP and some related results. In particular, we show that it is DP-complete to decide whether or not a given graph can be colored with exactly four colors, where DP is the second level of the boolean hierarchy. This result solves a question raised by Wagner in 1987, and its proof uses a clever reduction due to Guruswami and Khanna. Another result covered is due to Cai and Meyer: The graph minimal uncolorability problem is also DP-complete. Finally, similar results on various versions of the exact domatic number problem are discussed. Key Words: Boolean hierarchy, completeness, exact colorability, exact domatic num- ber, minimal uncolorability. Category: F.1.2, F.1.3, F.2.2, F.2.3 1 Introduction, Historical Notes, and Definitions This paper surveys completeness results in the levels of the boolean hierarchy over NP, with a special focus on Wagner’s work [Wag87]. His general technique for proving completeness in the boolean hierarchy levels—as well as in other NP classes such as P|| , the class of problems solvable via parallel access to NP— inspired much of the recent results in this area. -
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 -
IBM Research Report Derandomizing Arthur-Merlin Games And
H-0292 (H1010-004) October 5, 2010 Computer Science IBM Research Report Derandomizing Arthur-Merlin Games and Approximate Counting Implies Exponential-Size Lower Bounds Dan Gutfreund, Akinori Kawachi IBM Research Division Haifa Research Laboratory Mt. Carmel 31905 Haifa, Israel Research Division Almaden - Austin - Beijing - Cambridge - Haifa - India - T. J. Watson - Tokyo - Zurich LIMITED DISTRIBUTION NOTICE: This report has been submitted for publication outside of IBM and will probably be copyrighted if accepted for publication. It has been issued as a Research Report for early dissemination of its contents. In view of the transfer of copyright to the outside publisher, its distribution outside of IBM prior to publication should be limited to peer communications and specific requests. After outside publication, requests should be filled only by reprints or legally obtained copies of the article (e.g. , payment of royalties). Copies may be requested from IBM T. J. Watson Research Center , P. O. Box 218, Yorktown Heights, NY 10598 USA (email: [email protected]). Some reports are available on the internet at http://domino.watson.ibm.com/library/CyberDig.nsf/home . Derandomization Implies Exponential-Size Lower Bounds 1 DERANDOMIZING ARTHUR-MERLIN GAMES AND APPROXIMATE COUNTING IMPLIES EXPONENTIAL-SIZE LOWER BOUNDS Dan Gutfreund and Akinori Kawachi Abstract. We show that if Arthur-Merlin protocols can be deran- domized, then there is a Boolean function computable in deterministic exponential-time with access to an NP oracle, that cannot be computed by Boolean circuits of exponential size. More formally, if prAM ⊆ PNP then there is a Boolean function in ENP that requires circuits of size 2Ω(n). -
Different Approaches to Proof Systems
This is a repository copy of Different Approaches to Proof Systems. White Rose Research Online URL for this paper: http://eprints.whiterose.ac.uk/74802/ Proceedings Paper: Beyersdorff, O and Mueller, S (2010) Different Approaches to Proof Systems. In: Kratochvil, J, Li, A, Fiala, J and Kolman, P, (eds.) Theory and Applications of Models of Computation, 7th Annual Conference, TAMC 2010, Proceedings. 7th Annual Conference, TAMC 2010, 7 - 11 June 2010, Prague, Czech Republic. Springer Verlag , 50 - 59 . ISBN 978-3-642-13561-3 https://doi.org/10.1007/978-3-642-13562-0_6 Reuse See Attached Takedown If you consider content in White Rose Research Online to be in breach of UK law, please notify us by emailing [email protected] including the URL of the record and the reason for the withdrawal request. [email protected] https://eprints.whiterose.ac.uk/ Different Approaches to Proof Systems Olaf Beyersdorff1★ and Sebastian M¨uller2★★ 1 Institute of Computer Science, Humboldt University Berlin, Germany 2 Faculty of Mathematics and Physics, Charles University Prague, Czech Republic {beyersdo,smueller}@informatik.hu-berlin.de Abstract. The classical approach to proof complexity perceives proof systems as deterministic, uniform, surjective, polynomial-time computable functions that map strings to (propositional) tautologies. This approach has been intensively studied since the late 70’s and a lot of progress has been made. During the last years research was started investigating alternative notions of proof systems. There are interesting results stem- ming from dropping the uniformity requirement, allowing oracle access, using quantum computations, or employing probabilism. These lead to different notions of proof systems for which we survey recent results in this paper. -
Inaugural - Dissertation
INAUGURAL - DISSERTATION zur Erlangung der Doktorwurde¨ der Naturwissenschaftlich-Mathematischen Gesamtfakultat¨ der Ruprecht - Karls - Universitat¨ Heidelberg vorgelegt von Timur Bakibayev aus Karagandinskaya Tag der mundlichen¨ Prufung:¨ 10.03.2010 Weak Completeness Notions For Exponential Time Gutachter: Prof. Dr. Klaus Ambos-Spies Prof. Dr. Serikzhan Badaev Abstract The standard way for proving a problem to be intractable is to show that the problem is hard or complete for one of the standard complexity classes containing intractable problems. Lutz (1995) proposed a generalization of this approach by introducing more general weak hardness notions which still imply intractability. While a set A is hard for a class C if all problems in C can be reduced to A (by a polynomial-time bounded many-one reduction) and complete if it is hard and a member of C, Lutz proposed to call a set A weakly hard if a nonnegligible part of C can be reduced to A and to call A weakly complete if in addition A 2 C. For the exponential-time classes E = DTIME(2lin) and EXP = DTIME(2poly), Lutz formalized these ideas by introducing resource bounded (Lebesgue) measures on these classes and by saying that a subclass of E is negligible if it has measure 0 in E (and similarly for EXP). A variant of these concepts, based on resource bounded Baire category in place of measure, was introduced by Ambos-Spies (1996) where now a class is declared to be negligible if it is meager in the corresponding resource bounded sense. In our thesis we introduce and investigate new, more general, weak hardness notions for E and EXP and compare them with the above concepts from the litera- ture. -
Nondeterministic and Randomized Boolean Hierarchies in Communication Complexity
Electronic Colloquium on Computational Complexity, Report No. 43 (2019) Nondeterministic and Randomized Boolean Hierarchies in Communication Complexity Toniann Pitassi Morgan Shirley Thomas Watson University of Toronto and University of Toronto University of Memphis Institute for Advanced Study March 8, 2019 Abstract We study the Boolean Hierarchy in the context of two-party communication complexity, as well as the analogous hierarchy defined with one-sided error randomness instead of nondeterminism. Our results provide a complete picture of the relationships among complexity classes within and across these two hierarchies. In particular, we prove a query-to-communication lifting theorem for all levels of the Boolean Hierarchy and use it to resolve an open problem stated in the paper by Halstenberg and Reischuk (CCC 1988) which initiated this topic. 1 Introduction The Boolean Hierarchy in classical complexity theory consists of problems that have a polynomial-time algorithm making a constant number of queries to an NP oracle. This hierarchy has an intricate relationship with other complexity classes, and its second level (DP) captures the complexity of certain \exact" versions of optimization problems. It consists of an infinite sequence of complexity classes NP(q) for q = 1; 2; 3;::: (where NP(1) = NP and NP(2) = DP). Among the several equivalent ways of defining these classes [Wec85, CH86, KSW87, Wag88], perhaps the simplest is that NP(q) consists of all decision problems that can be computed by taking the parity of the answers to a sequence of q NP problems on the given input. As illustrated in NP[q] Figure 1, it is known that these levels are intertwined with the classes Pk of all decision problems solvable in polynomial time using q nonadaptive NP queries (for constant q)[KSW87, Wag88, Bei91]: NP[q] NP[q] NP(q) ⊆ Pk ⊆ NP(q + 1) and coNP(q) ⊆ Pk ⊆ coNP(q + 1) NP[q] (by closure of Pk under complementation).