6.845 Quantum Complexity Theory, Lecture 16
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Computational Complexity: a Modern Approach
i Computational Complexity: A Modern Approach Draft of a book: Dated January 2007 Comments welcome! Sanjeev Arora and Boaz Barak Princeton University [email protected] Not to be reproduced or distributed without the authors’ permission This is an Internet draft. Some chapters are more finished than others. References and attributions are very preliminary and we apologize in advance for any omissions (but hope you will nevertheless point them out to us). Please send us bugs, typos, missing references or general comments to [email protected] — Thank You!! DRAFT ii DRAFT Chapter 9 Complexity of counting “It is an empirical fact that for many combinatorial problems the detection of the existence of a solution is easy, yet no computationally efficient method is known for counting their number.... for a variety of problems this phenomenon can be explained.” L. Valiant 1979 The class NP captures the difficulty of finding certificates. However, in many contexts, one is interested not just in a single certificate, but actually counting the number of certificates. This chapter studies #P, (pronounced “sharp p”), a complexity class that captures this notion. Counting problems arise in diverse fields, often in situations having to do with estimations of probability. Examples include statistical estimation, statistical physics, network design, and more. Counting problems are also studied in a field of mathematics called enumerative combinatorics, which tries to obtain closed-form mathematical expressions for counting problems. To give an example, in the 19th century Kirchoff showed how to count the number of spanning trees in a graph using a simple determinant computation. Results in this chapter will show that for many natural counting problems, such efficiently computable expressions are unlikely to exist. -
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/. -
Arxiv:1506.08857V1 [Quant-Ph] 29 Jun 2015
Absolutely Maximally Entangled states, combinatorial designs and multi-unitary matrices Dardo Goyeneche National Quantum Information Center of Gda´nsk,81-824 Sopot, Poland and Faculty of Applied Physics and Mathematics, Technical University of Gda´nsk,80-233 Gda´nsk,Poland Daniel Alsina Dept. Estructura i Constituents de la Mat`eria,Universitat de Barcelona, Spain. Jos´e I. Latorre Dept. Estructura i Constituents de la Mat`eria,Universitat de Barcelona, Spain. and Center for Theoretical Physics, MIT, USA Arnau Riera ICFO-Institut de Ciencies Fotoniques, Castelldefels (Barcelona), Spain Karol Zyczkowski_ Institute of Physics, Jagiellonian University, Krak´ow,Poland and Center for Theoretical Physics, Polish Academy of Sciences, Warsaw, Poland (Dated: June 29, 2015) Absolutely Maximally Entangled (AME) states are those multipartite quantum states that carry absolute maximum entanglement in all possible partitions. AME states are known to play a relevant role in multipartite teleportation, in quantum secret sharing and they provide the basis novel tensor networks related to holography. We present alternative constructions of AME states and show their link with combinatorial designs. We also analyze a key property of AME, namely their relation to tensors that can be understood as unitary transformations in every of its bi-partitions. We call this property multi-unitarity. I. INTRODUCTION entropy S(ρ) = −Tr(ρ log ρ) ; (1) A complete characterization, classification and it is possible to show [1, 2] that the average entropy quantification of entanglement for quantum states re- of the reduced state σ = TrN=2j ih j to N=2 qubits mains an unfinished long-term goal in Quantum Infor- reads: N mation theory. -
Non-Unitary Quantum Computation in the Ground Space of Local Hamiltonians
Non-Unitary Quantum Computation in the Ground Space of Local Hamiltonians Na¨ıri Usher,1, ∗ Matty J. Hoban,2, 3 and Dan E. Browne1 1Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom. 2University of Oxford, Department of Computer Science, Wolfson Building, Parks Road, Oxford OX1 3QD, United Kingdom. 3School of Informatics, University of Edinburgh, 10 Crichton Street, Edinburgh EH8 9AB, UK A central result in the study of Quantum Hamiltonian Complexity is that the k-local hamilto- nian problem is QMA-complete [1]. In that problem, we must decide if the lowest eigenvalue of a Hamiltonian is bounded below some value, or above another, promised one of these is true. Given the ground state of the Hamiltonian, a quantum computer can determine this question, even if the ground state itself may not be efficiently quantum preparable. Kitaev's proof of QMA-completeness encodes a unitary quantum circuit in QMA into the ground space of a Hamiltonian. However, we now have quantum computing models based on measurement instead of unitary evolution, further- more we can use post-selected measurement as an additional computational tool. In this work, we generalise Kitaev's construction to allow for non-unitary evolution including post-selection. Fur- thermore, we consider a type of post-selection under which the construction is consistent, which we call tame post-selection. We consider the computational complexity consequences of this construc- tion and then consider how the probability of an event upon which we are post-selecting affects the gap between the ground state energy and the energy of the first excited state of its corresponding Hamiltonian. -
CS286.2 Lectures 5-6: Introduction to Hamiltonian Complexity, QMA-Completeness of the Local Hamiltonian Problem
CS286.2 Lectures 5-6: Introduction to Hamiltonian Complexity, QMA-completeness of the Local Hamiltonian problem Scribe: Jenish C. Mehta The Complexity Class BQP The complexity class BQP is the quantum analog of the class BPP. It consists of all languages that can be decided in quantum polynomial time. More formally, Definition 1. A language L 2 BQP if there exists a classical polynomial time algorithm A that ∗ maps inputs x 2 f0, 1g to quantum circuits Cx on n = poly(jxj) qubits, where the circuit is considered a sequence of unitary operators each on 2 qubits, i.e Cx = UTUT−1...U1 where each 2 2 Ui 2 L C ⊗ C , such that: 2 i. Completeness: x 2 L ) Pr(Cx accepts j0ni) ≥ 3 1 ii. Soundness: x 62 L ) Pr(Cx accepts j0ni) ≤ 3 We say that the circuit “Cx accepts jyi” if the first output qubit measured in Cxjyi is 0. More j0i specifically, letting P1 = j0ih0j1 be the projection of the first qubit on state j0i, j0i 2 Pr(Cx accepts jyi) =k (P1 ⊗ In−1)Cxjyi k2 The Complexity Class QMA The complexity class QMA (or BQNP, as Kitaev originally named it) is the quantum analog of the class NP. More formally, Definition 2. A language L 2 QMA if there exists a classical polynomial time algorithm A that ∗ maps inputs x 2 f0, 1g to quantum circuits Cx on n + q = poly(jxj) qubits, such that: 2q i. Completeness: x 2 L ) 9jyi 2 C , kjyik2 = 1, such that Pr(Cx accepts j0ni ⊗ 2 jyi) ≥ 3 2q 1 ii. -
Restricted Versions of the Two-Local Hamiltonian Problem
The Pennsylvania State University The Graduate School Eberly College of Science RESTRICTED VERSIONS OF THE TWO-LOCAL HAMILTONIAN PROBLEM A Dissertation in Physics by Sandeep Narayanaswami c 2013 Sandeep Narayanaswami Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy August 2013 The dissertation of Sandeep Narayanaswami was reviewed and approved* by the following: Sean Hallgren Associate Professor of Computer Science and Engineering Dissertation Adviser, Co-Chair of Committee Nitin Samarth Professor of Physics Head of the Department of Physics Co-Chair of Committee David S Weiss Professor of Physics Jason Morton Assistant Professor of Mathematics *Signatures are on file in the Graduate School. Abstract The Hamiltonian of a physical system is its energy operator and determines its dynamics. Un- derstanding the properties of the ground state is crucial to understanding the system. The Local Hamiltonian problem, being an extension of the classical Satisfiability problem, is thus a very well-motivated and natural problem, from both physics and computer science perspectives. In this dissertation, we seek to understand special cases of the Local Hamiltonian problem in terms of algorithms and computational complexity. iii Contents List of Tables vii List of Tables vii Acknowledgments ix 1 Introduction 1 2 Background 6 2.1 Classical Complexity . .6 2.2 Quantum Computation . .9 2.3 Generalizations of SAT . 11 2.3.1 The Ising model . 13 2.3.2 QMA-complete Local Hamiltonians . 13 2.3.3 Projection Hamiltonians, or Quantum k-SAT . 14 2.3.4 Commuting Local Hamiltonians . 14 2.3.5 Other special cases . 15 2.3.6 Approximation Algorithms and Heuristics . -
Complexity Theory
Complexity Theory Course Notes Sebastiaan A. Terwijn Radboud University Nijmegen Department of Mathematics P.O. Box 9010 6500 GL Nijmegen the Netherlands [email protected] Copyright c 2010 by Sebastiaan A. Terwijn Version: December 2017 ii Contents 1 Introduction 1 1.1 Complexity theory . .1 1.2 Preliminaries . .1 1.3 Turing machines . .2 1.4 Big O and small o .........................3 1.5 Logic . .3 1.6 Number theory . .4 1.7 Exercises . .5 2 Basics 6 2.1 Time and space bounds . .6 2.2 Inclusions between classes . .7 2.3 Hierarchy theorems . .8 2.4 Central complexity classes . 10 2.5 Problems from logic, algebra, and graph theory . 11 2.6 The Immerman-Szelepcs´enyi Theorem . 12 2.7 Exercises . 14 3 Reductions and completeness 16 3.1 Many-one reductions . 16 3.2 NP-complete problems . 18 3.3 More decision problems from logic . 19 3.4 Completeness of Hamilton path and TSP . 22 3.5 Exercises . 24 4 Relativized computation and the polynomial hierarchy 27 4.1 Relativized computation . 27 4.2 The Polynomial Hierarchy . 28 4.3 Relativization . 31 4.4 Exercises . 32 iii 5 Diagonalization 34 5.1 The Halting Problem . 34 5.2 Intermediate sets . 34 5.3 Oracle separations . 36 5.4 Many-one versus Turing reductions . 38 5.5 Sparse sets . 38 5.6 The Gap Theorem . 40 5.7 The Speed-Up Theorem . 41 5.8 Exercises . 43 6 Randomized computation 45 6.1 Probabilistic classes . 45 6.2 More about BPP . 48 6.3 The classes RP and ZPP . -
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 :::::::::::: -
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. -
Arxiv:Quant-Ph/0612113V1 14 Dec 2006 † ∗ Nalo Hs Rosi Euto Ewe Unu Icisadtim and Circuits Quantum C Between Reduction QMA a Is Is Hamiltonian Complete
A new construction for a QMA complete 3-local Hamiltonian. 1, , Daniel Nagaj ∗ and Shay Mozes † 1Center for Theoretical Physics, MIT, Cambridge, MA 02139 (Dated: December 12, 2006) We present a new way of encoding a quantum computation into a 3-local Hamiltonian. Our construction is novel in that it does not include any terms that induce legal-illegal clock transitions. Therefore, the weights of the terms in the Hamiltonian do not scale with the size of the problem as in previous constructions. This improves the construction by Kempe and Regev [2], who were the first to prove that 3-local Hamiltonian is complete for the complexity class QMA, the quantum analogue of NP. Quantum k-SAT, a restricted version of the local Hamiltonian problem using only projector terms, was introduced by Bravyi [1] as an analogue of the classical k-SAT problem. Bravyi proved that quantum 4-SAT is complete for the class QMA with one-sided error (QMA1) and that quantum 2-SAT is in P. We give an encoding of a quantum circuit into a quantum 4-SAT Hamiltonian using only 3-local terms. As an intermediate step to this 3-local construction, we show that quantum 3-SAT for particles with dimensions 3 × 2 × 2 (a qutrit and two qubits) is QMA1 complete. The complexity of quantum 3-SAT with qubits remains an open question. I. INTRODUCTION In recent years, quantum complexity classes have been defined and studied in an attempt to understand the capacity and limitations of quantum computers and quantum algorithms and their relation to classical complexity classes. -
1 Class PP(Probabilistic Poly-Time) 2 Complete Problems for BPP?
E0 224: Computational Complexity Theory Instructor: Chandan Saha Lecture 20 22 October 2014 Scribe: Abhijat Sharma 1 Class PP(Probabilistic Poly-time) Recall that when we define the class BPP, we have to enforce the condition that the success probability of the PTM is bounded, "strictly" away from 1=2 (in our case, we have chosen a particular value 2=3). Now, we try to explore another class of languages where the success (or failure) probability can be very close to 1=2 but still equality is not possible. Definition 1 A language L ⊆ f0; 1g∗ is said to be in PP if there exists a polynomial time probabilistic turing machine M, such that P rfM(x) = L(x)g > 1=2 Thus, it is clear from the above definition that BPP ⊆ PP, however there are problems in PP that are much harder than those in BPP. Some examples of such problems are closely related to the counting versions of some problems, whose decision problems are in NP. For example, the problem of counting how many satisfying assignments exist for a given boolean formula. We will discuss this class in more details, when we discuss the complexity of counting problems. 2 Complete problems for BPP? After having discussed many properties of the class BPP, it is natural to ask whether we have any BPP- complete problems. Unfortunately, we do not know of any complete problem for the class BPP. Now, we examine why we appears tricky to define complete problems for BPP in the same way as other complexity classes. -
Adiabatic Quantum Computation Is Equivalent To
SIAM REVIEW c 2008 Society for Industrial and Applied Mathematics Vol. 50, No. 4, pp. 755–787 Adiabatic Quantum Computation Is Equivalent to ∗ Standard Quantum Computation Dorit Aharonov† Wim van Dam‡ Julia Kempe§ Zeph Landau¶ Seth Lloyd Oded Regev§ Abstract. The model of adiabatic quantum computation is a relatively recent model of quantum com- putation that has attracted attention in the physics and computer science communities. We describe an efficient adiabatic simulation of any given quantum circuit. This implies that the adiabatic computation model and the standard circuit-based quantum compu- tation model are polynomially equivalent. Our result can be extended to the physically realistic setting of particles arranged on a two-dimensional grid with nearest neighbor inter- actions. The equivalence between the models allows one to state the main open problems in quantum computation using well-studied mathematical objects such as eigenvectors and spectral gaps of Hamiltonians. Key words. quantum computation,adiabatic computation,nearest neighbor interactions AMS subject classifications. 81P68,68Q05 DOI. 10.1137/080734479 1. Introduction. The model of quantum computation has been thoroughly in- vestigated in the last two decades and is by now a well-established one [47]. In this ∗ Published electronically November 5,2008. This paper originally appeared in SIAM Journal on Computing,Volume 37,Number 1,2007,pages 166–194. This work was performed by an em- ployee of the U.S. Government or under U.S. Government contract. The U.S. Government retains a nonexclusive,royalty-free license to publish or reproduce the published form of this contribution, or allow others to do so,for U.S.