CS286.2 Around the Quantum PCP Conjecture Challenge Problem
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i Computational Complexity: A Modern Approach Sanjeev Arora and Boaz Barak Princeton University http://www.cs.princeton.edu/theory/complexity/ [email protected] Not to be reproduced or distributed without the authors’ permission ii Chapter 11 PCP Theorem and Hardness of Approximation: An introduction “...most problem reductions do not create or preserve such gaps...To create such a gap in the generic reduction (cf. Cook)...also seems doubtful. The in- tuitive reason is that computation is an inherently unstable, non-robust math- ematical object, in the the sense that it can be turned from non-accepting to accepting by changes that would be insignificant in any reasonable metric.” Papadimitriou and Yannakakis [PY88] This chapter describes the PCP Theorem, a surprising discovery of complexity theory, with many implications to algorithm design. Since the discovery of NP-completeness in 1972 researchers had mulled over the issue of whether we can efficiently compute approxi- mate solutions to NP-hard optimization problems. They failed to design such approxima- tion algorithms for most problems (see Section 1.1 for an introduction to approximation algorithms). They then tried to show that computing approximate solutions is also hard, but apart from a few isolated successes this effort also stalled. Researchers slowly began to realize that the Cook-Levin-Karp style reductions do not suffice to prove any limits on ap- proximation algorithms (see the above quote from an influential Papadimitriou-Yannakakis paper that appeared a few years before the discoveries described in this chapter). The PCP theorem, discovered in 1992, gave a new definition of NP and provided a new starting point for reductions. -
On Uniformity Within NC
On Uniformity Within NC David A Mix Barrington Neil Immerman HowardStraubing University of Massachusetts University of Massachusetts Boston Col lege Journal of Computer and System Science Abstract In order to study circuit complexity classes within NC in a uniform setting we need a uniformity condition which is more restrictive than those in common use Twosuch conditions stricter than NC uniformity RuCo have app eared in recent research Immermans families of circuits dened by rstorder formulas ImaImb and a unifor mity corresp onding to Buss deterministic logtime reductions Bu We show that these two notions are equivalent leading to a natural notion of uniformity for lowlevel circuit complexity classes Weshow that recent results on the structure of NC Ba still hold true in this very uniform setting Finallyweinvestigate a parallel notion of uniformity still more restrictive based on the regular languages Here we givecharacterizations of sub classes of the regular languages based on their logical expressibility extending recentwork of Straubing Therien and Thomas STT A preliminary version of this work app eared as BIS Intro duction Circuit Complexity Computer scientists have long tried to classify problems dened as Bo olean predicates or functions by the size or depth of Bo olean circuits needed to solve them This eort has Former name David A Barrington Supp orted by NSF grant CCR Mailing address Dept of Computer and Information Science U of Mass Amherst MA USA Supp orted by NSF grants DCR and CCR Mailing address Dept of -
Lecture 19,20 (Nov 15&17, 2011): Hardness of Approximation, PCP
,20 CMPUT 675: Approximation Algorithms Fall 2011 Lecture 19,20 (Nov 15&17, 2011): Hardness of Approximation, PCP theorem Lecturer: Mohammad R. Salavatipour Scribe: based on older notes 19.1 Hardness of Approximation So far we have been mostly talking about designing approximation algorithms and proving upper bounds. From no until the end of the course we will be talking about proving lower bounds (i.e. hardness of approximation). We are familiar with the theory of NP-completeness. When we prove that a problem is NP-hard it implies that, assuming P=NP there is no polynomail time algorithm that solves the problem (exactly). For example, for SAT, deciding between Yes/No is hard (again assuming P=NP). We would like to show that even deciding between those instances that are (almost) satisfiable and those that are far from being satisfiable is also hard. In other words, create a gap between Yes instances and No instances. These kinds of gaps imply hardness of approximation for optimization version of NP-hard problems. In fact the PCP theorem (that we will see next lecture) is equivalent to the statement that MAX-SAT is NP- hard to approximate within a factor of (1 + ǫ), for some fixed ǫ> 0. Most of hardness of approximation results rely on PCP theorem. For proving a hardness, for example for vertex cover, PCP shows that the existence of following reduction: Given a formula ϕ for SAT, we build a graph G(V, E) in polytime such that: 2 • if ϕ is a yes-instance, then G has a vertex cover of size ≤ 3 |V |; 2 • if ϕ is a no-instance, then every vertex cover of G has a size > α 3 |V | for some fixed α> 1. -
Two-Way Automata Characterizations of L/Poly Versus NL
Two-way automata characterizations of L/poly versus NL Christos A. Kapoutsis1;? and Giovanni Pighizzini2 1 LIAFA, Universit´eParis VII, France 2 DICo, Universit`adegli Studi di Milano, Italia Abstract. Let L/poly and NL be the standard complexity classes, of languages recognizable in logarithmic space by Turing machines which are deterministic with polynomially-long advice and nondeterministic without advice, respectively. We recast the question whether L/poly ⊇ NL in terms of deterministic and nondeterministic two-way finite automata (2dfas and 2nfas). We prove it equivalent to the question whether every s-state unary 2nfa has an equivalent poly(s)-state 2dfa, or whether a poly(h)-state 2dfa can check accessibility in h-vertex graphs (even under unary encoding) or check two-way liveness in h-tall, h-column graphs. This complements two recent improvements of an old theorem of Berman and Lingas. On the way, we introduce new types of reductions between regular languages (even unary ones), use them to prove the completeness of specific languages for two-way nondeterministic polynomial size, and propose a purely combinatorial conjecture that implies L/poly + NL. 1 Introduction A prominent open question in complexity theory asks whether nondeterminism is essential in logarithmic-space Turing machines. Formally, this is the question whether L = NL, for L and NL the standard classes of languages recognizable by logarithmic-space deterministic and nondeterministic Turing machines. In the late 70's, Berman and Lingas [1] connected this question to the comparison between deterministic and nondeterministic two-way finite automata (2dfas and 2nfas), proving that if L = NL, then for every s-state σ-symbol 2nfa there is a poly(sσ)-state 2dfa which agrees with it on all inputs of length ≤ s. -
Pointer Quantum Pcps and Multi-Prover Games
Pointer Quantum PCPs and Multi-Prover Games Alex B. Grilo∗1, Iordanis Kerenidis†1,2, and Attila Pereszlényi‡1 1IRIF, CNRS, Université Paris Diderot, Paris, France 2Centre for Quantum Technologies, National University of Singapore, Singapore 1st March, 2016 Abstract The quantum PCP (QPCP) conjecture states that all problems in QMA, the quantum analogue of NP, admit quantum verifiers that only act on a constant number of qubits of a polynomial size quantum proof and have a constant gap between completeness and soundness. Despite an impressive body of work trying to prove or disprove the quantum PCP conjecture, it still remains widely open. The above-mentioned proof verification statement has also been shown equivalent to the QMA-completeness of the Local Hamiltonian problem with constant relative gap. Nevertheless, unlike in the classical case, no equivalent formulation in the language of multi-prover games is known. In this work, we propose a new type of quantum proof systems, the Pointer QPCP, where a verifier first accesses a classical proof that he can use as a pointer to which qubits from the quantum part of the proof to access. We define the Pointer QPCP conjecture, that states that all problems in QMA admit quantum verifiers that first access a logarithmic number of bits from the classical part of a polynomial size proof, then act on a constant number of qubits from the quantum part of the proof, and have a constant gap between completeness and soundness. We define a new QMA-complete problem, the Set Local Hamiltonian problem, and a new restricted class of quantum multi-prover games, called CRESP games. -
The Boolean Formula Value Problem Is in ALOGTIME (Preliminary Version)
The Boolean formula value problem is in ALOGTIME (Preliminary Version) Samuel R. Buss¤ Department of Mathematics University of California, Berkeley January 1987 Abstract The Boolean formula value problem is in alternating log time and, more generally, parenthesis context-free languages are in alternating log time. The evaluation of reverse Polish notation Boolean formulas is also in alternating log time. These results are optimal since the Boolean formula value problem is complete for alternating log time under deterministic log time reductions. Consequently, it is also complete for alternating log time under AC0 reductions. 1. Introduction The Boolean formula value problem is to determine the truth value of a variable-free Boolean formula, or equivalently, to recognize the true Boolean sentences. N. Lynch [11] gave log space algorithms for the Boolean formula value problem and for the more general problem of recognizing a parenthesis context-free grammar. This paper shows that these problems have alternating log time algorithms. This answers the question of Cook [5] of whether the Boolean formula value problem is log space complete | it is not, unless log space and alternating log time are identical. Our results are optimal since, for an appropriately de¯ned notion of log time reductions, the Boolean formula value problem is complete for alternating log time under deterministic log time reductions; consequently, it is also complete for alternating log time under AC0 reductions. It follows that the Boolean formula value problem is not in the log time hierarchy. There are two reasons why the Boolean formula value problem is interesting. First, a Boolean (or propositional) formula is a very fundamental concept ¤Supported in part by an NSF postdoctoral fellowship. -
Probabilistic Proof Systems: a Primer
Probabilistic Proof Systems: A Primer Oded Goldreich Department of Computer Science and Applied Mathematics Weizmann Institute of Science, Rehovot, Israel. June 30, 2008 Contents Preface 1 Conventions and Organization 3 1 Interactive Proof Systems 4 1.1 Motivation and Perspective ::::::::::::::::::::::: 4 1.1.1 A static object versus an interactive process :::::::::: 5 1.1.2 Prover and Veri¯er :::::::::::::::::::::::: 6 1.1.3 Completeness and Soundness :::::::::::::::::: 6 1.2 De¯nition ::::::::::::::::::::::::::::::::: 7 1.3 The Power of Interactive Proofs ::::::::::::::::::::: 9 1.3.1 A simple example :::::::::::::::::::::::: 9 1.3.2 The full power of interactive proofs ::::::::::::::: 11 1.4 Variants and ¯ner structure: an overview ::::::::::::::: 16 1.4.1 Arthur-Merlin games a.k.a public-coin proof systems ::::: 16 1.4.2 Interactive proof systems with two-sided error ::::::::: 16 1.4.3 A hierarchy of interactive proof systems :::::::::::: 17 1.4.4 Something completely di®erent ::::::::::::::::: 18 1.5 On computationally bounded provers: an overview :::::::::: 18 1.5.1 How powerful should the prover be? :::::::::::::: 19 1.5.2 Computational Soundness :::::::::::::::::::: 20 2 Zero-Knowledge Proof Systems 22 2.1 De¯nitional Issues :::::::::::::::::::::::::::: 23 2.1.1 A wider perspective: the simulation paradigm ::::::::: 23 2.1.2 The basic de¯nitions ::::::::::::::::::::::: 24 2.2 The Power of Zero-Knowledge :::::::::::::::::::::: 26 2.2.1 A simple example :::::::::::::::::::::::: 26 2.2.2 The full power of zero-knowledge proofs :::::::::::: -
Lecture 19: Interactive Proofs and the PCP Theorem
Lecture 19: Interactive Proofs and the PCP Theorem Valentine Kabanets November 29, 2016 1 Interactive Proofs In this model, we have an all-powerful Prover (with unlimited computational prover) and a polytime Verifier. Given a common input x, the prover and verifier exchange some messages, and at the end of this interaction, Verifier accepts or rejects. Ideally, we want the verifier accept an input in the language, and reject the input not in the language. For example, if the language is SAT, then Prover can convince Verifier of satisfiability of the given formula by simply presenting a satisfying assignment. On the other hand, if the formula is unsatisfiable, then any \proof" of Prover will be rejected by Verifier. This is nothing but the reformulation of the definition of NP. We can consider more complex languages. For instance, UNSAT is the set of unsatisfiable formulas. Can we design a protocol so that if the given formula is unsatisfiable, Prover can convince Verifier to accept; otherwise, Verifier will reject? We suspect that no such protocol is possible if Verifier is restricted to be a deterministic polytime algorithm. However, if we allow Verifier to be a randomized polytime algorithm, then indeed such a protocol can be designed. In fact, we'll show that every language in PSPACE has an interactive protocol. But first, we consider a simpler problem #SAT of counting the number of satisfying assignments to a given formula. 1.1 Interactive Proof Protocol for #SAT The problem is #SAT: Given a cnf-formula φ(x1; : : : ; xn), compute the number of satisfying as- signments of φ. -
Relationships Among PL, #L, and the Determinant
Relationships Among PL, L, and the Determinant y z Eric Allender Mitsunori Ogihara Department of Computer Science Department of Computer Science Hill Center, Busch Campus, P.O. Box 1179 UniversityofRochester Piscataway, NJ 08855-117 9, USA Ro chester, NY 14627 [email protected] [email protected] Abstract Recent results byTo da, Vinay, Damm, and Valianthave shown that the complexity of the determinantischaracterized by the complexity of counting the numb er of accepting compu- tations of a nondeterministic logspace-b ounded machine. This class of functions is known as L. By using that characterization and by establishing a few elementary closure prop erties, we givea very simple pro of of a theorem of Jung, showing that probabilistic logspace-b ounded PL machines lose none of their computational p ower if they are restricted to run in p olynomial time. We also present new results comparing and contrasting the classes of functions reducible to PL, L, and the determinant, using various notions of reducibility. 1 Intro duction One of the most imp ortant and in uential early results of complexity theory is the theorem of [47] showing that the complexity of computing the p ermanentofaninteger matrix is characterized by the complexity class P of functions that count the numb er of accepting computation paths of a nondeterministic p olynomial-time machine. It is p erhaps surprising that well over a decade passed b efore it was discovered that an equally-close connection exists b etween the complexityof computing the determinant of a matrix and the class L de ned in [5] of functions that count the numb er of accepting computation paths of a nondeterministic logspace-b ounded machine. -
On a Theory of Computation and Complexity Over the Real Numbers: Np-Completeness, Recursive Functions and Universal Machines1
BULLETIN (New Series) OF THE AMERICAN MATHEMATICAL SOCIETY Volume 21, Number 1, July 1989 ON A THEORY OF COMPUTATION AND COMPLEXITY OVER THE REAL NUMBERS: NP-COMPLETENESS, RECURSIVE FUNCTIONS AND UNIVERSAL MACHINES1 LENORE BLUM2, MIKE SHUB AND STEVE SMALE ABSTRACT. We present a model for computation over the reals or an arbitrary (ordered) ring R. In this general setting, we obtain universal machines, partial recursive functions, as well as JVP-complete problems. While our theory reflects the classical over Z (e.g., the computable func tions are the recursive functions) it also reflects the special mathematical character of the underlying ring R (e.g., complements of Julia sets provide natural examples of R. E. undecidable sets over the reals) and provides a natural setting for studying foundational issues concerning algorithms in numerical analysis. Introduction. We develop here some ideas for machines and computa tion over the real numbers R. One motivation for this comes from scientific computation. In this use of the computer, a reasonable idealization has the cost of multiplication independent of the size of the number. This contrasts with the usual theoretical computer science picture which takes into account the number of bits of the numbers. Another motivation is to bring the theory of computation into the do main of analysis, geometry and topology. The mathematics of these sub jects can then be put to use in the systematic analysis of algorithms. On the other hand, there is an extensively developed subject of the theory of discrete computation, which we don't wish to lose in our theory. -
QMA with Subset State Witnesses
QMA with subset state witnesses Alex B. Grilo1, Iordanis Kerenidis1,2, and Jamie Sikora2 1 LIAFA, CNRS, Universite´ Paris Diderot, Paris, France 2Centre for Quantum Technologies, National University of Singapore, Singapore One of the notions at the heart of classical complexity theory is the class NP and the fact that deciding whether a boolean formula is satisfiable or not is NP-complete [6, 13]. The importance of NP-completeness became apparent through the plethora of combinatorial problems that can be cast as constraint satisfaction problems and shown to be NP-complete. Moreover, the famous PCP theorem [3, 4] provided a new, surprising description of the class NP: any language in NP can be verified efficiently by accessing probabilistically a constant number of bits of a polynomial- size witness. This opened the way to showing that in many cases, approximating the solution of NP-hard problems remains as hard as solving them exactly. An equivalent definition of the PCP theorem states that it remains NP-hard to decide whether an instance of a constraint satisfaction problem is satisfiable or any assignement violates at least a constant fraction of the constraints. Not surprisingly, the quantum analog of the class NP, defined by Kitaev [11] and called QMA, has been the subject of extensive study in the last decade. Many important properties of this class are known, including a strong amplification property and an upper bound of PP [14], as well as a number of complete problems related to the ground state energy of different types of Hamiltonians [11, 10, 15, 7, 8]. -
Formal Theories for Linear Algebra
Formal Theories for Linear Algebra Stephen Cook and Lila Fontes Department of Computer Science, University of Toronto fsacook, [email protected] Abstract. We introduce two-sorted theories in the style of [CN10] for the complexity classes ⊕L and DET , whose complete problems include determinants over Z2 and Z, respectively. We then describe interpreta- tions of Soltys' linear algebra theory LAp over arbitrary integral domains, into each of our new theories. The result shows equivalences of standard theorems of linear algebra over Z2 and Z can be proved in the corre- sponding theory, but leaves open the interesting question of whether the theorems themselves can be proved. 1 Introduction This paper introduces formal theories for the complexity classes ⊕L (also called P arityL) and DET for reasoning about linear algebra over the rings Z2 and Z, respectively. Complete problems for these classes include standard computa- tional problems of linear algebra over their respective rings, such as computing determinants, matrix powers, and coefficients of the characteristic polynomial of a matrix [BDHM92]. (Recently [BKR09] proved that for each k ≥ 1, computing the permanent mod 2k of an integer matrix is in ⊕L, and hence complete.) Each theory allows induction over any relation in the associated complexity class ⊕L or DET , and the functions definable in each theory are the functions in the class. Thus determinants and characteristic polynomials can be defined in the theories, but it is not clear that their standard properties can be proved without defining concepts outside the associated complexity classes. This remains an interesting open question [SK01,SC04].