Thesis Is Owed to Him, Either Directly Or Indirectly

Total Page:16

File Type:pdf, Size:1020Kb

Thesis Is Owed to Him, Either Directly Or Indirectly UvA-DARE (Digital Academic Repository) Quantum Computing and Communication Complexity de Wolf, R.M. Publication date 2001 Document Version Final published version Link to publication Citation for published version (APA): de Wolf, R. M. (2001). Quantum Computing and Communication Complexity. Institute for Logic, Language and Computation. General rights It is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), other than for strictly personal, individual use, unless the work is under an open content license (like Creative Commons). Disclaimer/Complaints regulations If you believe that digital publication of certain material infringes any of your rights or (privacy) interests, please let the Library know, stating your reasons. In case of a legitimate complaint, the Library will make the material inaccessible and/or remove it from the website. Please Ask the Library: https://uba.uva.nl/en/contact, or a letter to: Library of the University of Amsterdam, Secretariat, Singel 425, 1012 WP Amsterdam, The Netherlands. You will be contacted as soon as possible. UvA-DARE is a service provided by the library of the University of Amsterdam (https://dare.uva.nl) Download date:30 Sep 2021 Quantum Computing and Communication Complexity Ronald de Wolf Quantum Computing and Communication Complexity ILLC Dissertation Series 2001-06 For further information about ILLC-publications, please contact Institute for Logic, Language and Computation Universiteit van Amsterdam Plantage Muidergracht 24 1018 TV Amsterdam phone: +31-20-525 6051 fax: +31-20-525 5206 e-mail: [email protected] homepage: http://www.illc.uva.nl/ Quantum Computing and Communication Complexity Academisch Proefschrift ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magni¯cus prof.dr. J.J.M. Franse ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Aula der Universiteit op donderdag 6 september 2001, te 12.00 uur door Ronald Michiel de Wolf geboren te Zaandam. Promotiecommissie: Promotores: Prof.dr. H.M. Buhrman Prof.dr.ir. P.M.B. Vit¶anyi Overige leden: Prof.dr. R.E. Cleve Dr. M. Santha Prof.dr. A. Schrijver Dr. L. Torenvliet Faculteit der Natuurwetenschappen, Wiskunde en Informatica Copyright c 2001 by Ronald de Wolf ° ISBN: 90{5776{068{1 Se non `e vero, `e ben trovato. v Contents Acknowledgments xiii 1 Quantum Computing 1 1.1 Introduction . 1 1.2 Quantum Mechanics . 2 1.2.1 Superposition . 3 1.2.2 Measurement . 3 1.2.3 Unitary evolution . 4 1.3 Quantum Memory . 5 1.4 Quantum Computation . 7 1.4.1 Classical circuits . 7 1.4.2 Quantum circuits . 8 1.5 The Early Algorithms . 10 1.5.1 Deutsch-Jozsa . 11 1.5.2 Bernstein-Vazirani . 13 1.5.3 Simon . 13 1.6 Shor's Factoring Algorithm . 15 1.6.1 Reduction from factoring to period-¯nding . 15 1.6.2 The quantum Fourier transform . 16 1.6.3 Period-¯nding, easy case: r divides q . 17 1.6.4 Period-¯nding, hard case: r does not divide q . 19 1.7 Grover's Search Algorithm . 19 1.8 Summary . 21 I Query Complexity 23 2 Lower Bounds by Polynomials 25 vii 2.1 Introduction . 25 2.2 Boolean Functions and Polynomials . 27 2.2.1 Boolean functions . 27 2.2.2 Multilinear polynomials . 27 2.2.3 Representing and approximating functions . 28 2.3 Query Complexity . 31 2.3.1 Deterministic . 31 2.3.2 Randomized . 31 2.3.3 Quantum . 33 2.4 Degree Lower Bounds on Query Complexity . 34 2.5 Polynomial Relation for All Total Functions . 36 2.5.1 Certi¯cate complexity and block sensitivity . 36 2.5.2 Polynomial bound for QE(f) and Q0(f) . 39 2.5.3 Polynomial bound for Q2(f) . 41 2.6 Symmetric Functions . 42 2.6.1 Tight bounds . 43 2.6.2 OR . 44 2.6.3 PARITY . 45 2.6.4 MAJORITY . 46 2.7 Monotone Functions . 46 2.7.1 Improvements of the general bounds . 46 2.7.2 Tight bounds for zero-error . 47 2.7.3 Monotone graph properties . 49 2.8 Summary . 51 3 Bounds for Quantum Search 53 3.1 Introduction . 53 3.2 Tight Bounds for Unordered Search . 55 3.3 Application to Success Ampli¯cation . 60 3.4 Lower Bound for Ordered Searching . 61 3.4.1 Intuition . 61 3.4.2 Simulating queries to an ordered input . 62 3.4.3 Lower bound for ordered search . 65 3.5 Summary . 66 4 Element Distinctness and Related Problems 67 4.1 Introduction . 67 4.2 Finding Claws if f and g Are not Ordered . 69 4.3 Finding Claws if f is Ordered . 72 4.4 Finding Claws if both f and g Are Ordered . 72 4.5 Finding a Triangle in a Graph . 74 4.6 Summary . 75 viii 5 Average-Case and Non-Deterministic Query Complexity 77 5.1 Introduction . 77 5.2 Average-Case Complexity: De¯nitions . 78 5.3 Average-Case: Deterministic vs. Bounded-Error . 80 5.4 Average-Case: Randomized vs. Quantum . 81 5.4.1 The function . 81 5.4.2 Quantum upper bound . 82 5.4.3 Classical lower bound . 84 5.4.4 Worst-case quantum complexity of f . 85 5.5 Further Average-Case Results . 86 5.5.1 Super-exponential gap for non-uniform ¹ . 86 5.5.2 General bounds . 86 5.5.3 MAJORITY . 87 5.5.4 PARITY . 90 5.6 Non-Deterministic Complexity: De¯nitions . 90 5.7 Non-Deterministic Complexity: Characterization and Separation . 92 5.7.1 Non-deterministic polynomials . 93 5.7.2 Characterization of N(f) and NQ(f) . 94 5.7.3 Separations . 95 5.7.4 Relation of NQ(f) to other complexities . 96 5.8 Summary . 97 II Communication and Complexity 99 6 Quantum Communication Complexity 101 6.1 Introduction . 101 6.2 Quantum Communication . 102 6.3 The Model of Communication Complexity . 104 6.3.1 Classical . 104 6.3.2 Quantum . 106 6.4 Quantum Upper Bounds . 108 6.4.1 Initial steps . 108 6.4.2 Buhrman, Cleve, and Wigderson . 108 6.4.3 Raz . 110 6.5 Some Applications . 111 6.6 Other Developments . 112 6.7 Summary . 113 7 Lower Bounds for Quantum Communication Complexity 115 7.1 Introduction . 115 7.2 Lower Bounds for Exact Protocols . 117 7.3 A Lower Bound Technique via Polynomials . 121 ix 7.3.1 Decompositions and polynomials . 121 7.3.2 Symmetric functions . 123 7.3.3 Monotone functions . 126 7.4 Lower Bounds for Bounded-Error Protocols . 127 7.5 Non-Deterministic Complexity . 133 7.5.1 Some de¯nitions . 133 7.5.2 Equality to non-deterministic rank . 134 7.5.3 Exponential quantum-classical separation . 135 7.6 Open Problems . 136 7.7 Summary . 137 8 Quantum Fingerprinting 139 8.1 Introduction . 139 8.2 Simultaneous Message Passing . 141 8.3 Short Near-Orthogonal Quantum States . 145 8.4 The State Distinguishing Problem . 146 8.5 Exactly Orthogonal Quantum States . 148 8.6 Exact Fingerprinting with a Quantum Key . 150 8.7 Quantum Data Structures . 151 8.7.1 The quantum case . 151 8.7.2 Comparison with the classical case . 153 8.8 Summary . 154 9 Private Quantum Channels 155 9.1 Introduction . 155 9.2 Preliminaries . 157 9.2.1 Mixed states and superoperators . 157 9.2.2 Von Neumann entropy . 158 9.3 De¯nition of Private Quantum Channel . 159 9.4 Examples and Properties of PQCs . 160 9.5 Lower Bound on the Entropy of PQCs . 164 9.6 Summary . 167 A Some Useful Linear Algebra 169 A.1 Some Terminology and Notation . 169 A.2 Unitary Matrices . 170 A.3 Diagonalization and Singular.
Recommended publications
  • On the Tightness of the Buhrman-Cleve-Wigderson Simulation
    On the tightness of the Buhrman-Cleve-Wigderson simulation Shengyu Zhang Department of Computer Science and Engineering, The Chinese University of Hong Kong. [email protected] Abstract. Buhrman, Cleve and Wigderson gave a general communica- 1 1 n n tion protocol for block-composed functions f(g1(x ; y ); ··· ; gn(x ; y )) by simulating a decision tree computation for f [3]. It is also well-known that this simulation can be very inefficient for some functions f and (g1; ··· ; gn). In this paper we show that the simulation is actually poly- nomially tight up to the choice of (g1; ··· ; gn). This also implies that the classical and quantum communication complexities of certain block- composed functions are polynomially related. 1 Introduction Decision tree complexity [4] and communication complexity [7] are two concrete models for studying computational complexity. In [3], Buhrman, Cleve and Wigderson gave a general method to design communication 1 1 n n protocol for block-composed functions f(g1(x ; y ); ··· ; gn(x ; y )). The basic idea is to simulate the decision tree computation for f, with queries i i of the i-th variable answered by a communication protocol for gi(x ; y ). In the language of complexity measures, this result gives CC(f(g1; ··· ; gn)) = O~(DT(f) · max CC(gi)) (1) i where CC and DT are the communication complexity and the decision tree complexity, respectively. This simulation holds for all the models: deterministic, randomized and quantum1. It is also well known that the communication protocol by the above simulation can be very inefficient.
    [Show full text]
  • Noise in Quantum and Classical Computation & Non-Locality
    Noise in Quantum and Classical Computation & Non-locality Falk Unger Noise in Quantum and Classical Computation & Non-locality ILLC Dissertation Series DS-2008-06 For further information about ILLC-publications, please contact Institute for Logic, Language and Computation Universiteit van Amsterdam Plantage Muidergracht 24 1018 TV Amsterdam phone: +31-20-525 6051 fax: +31-20-525 5206 e-mail: [email protected] homepage: http://www.illc.uva.nl/ Noise in Quantum and Classical Computation & Non-locality Academisch Proefschrift ter verkrijging van de graad van doctor aan de Universiteit van Amsterdam op gezag van de Rector Magnificus prof. dr. D.C. van den Boom ten overstaan van een door het college voor promoties ingestelde commissie, in het openbaar te verdedigen in de Agnietenkapel op donderdag 18 september 2008, te 10.00 uur door Falk Peter Unger geboren te Dresden, Duitsland. Promotiecommissie: Promotor: prof. dr. H.M. Buhrman Overige Leden: prof. dr. R.E. Cleve prof. dr. R.D. Gill prof. dr. A. Schrijver dr. L. Torenvliet dr. R.M. de Wolf Faculteit der Natuurwetenschappen, Wiskunde en Informatica The investigations were financially supported by the EU project QAP (IST- 2005-15848) and by the Netherlands Organisation for Scientific Research (NWO) in the framework of the Innovational Research Incentives Scheme (in Dutch: Vernieuwingsimpuls), Vici-project 639.023.302. Copyright c 2008 by Falk Unger Printed and bound by PrintPartners Ipskamp. ISBN: 90-6196-547-0 The results in this thesis are based on the following articles Chapter 3 The results
    [Show full text]
  • Efficient Quantum Algorithms for Simulating Lindblad Evolution
    Efficient Quantum Algorithms for Simulating Lindblad Evolution∗ Richard Cleveyzx Chunhao Wangyz Abstract We consider the natural generalization of the Schr¨odingerequation to Markovian open system dynamics: the so-called the Lindblad equation. We give a quantum algorithm for simulating the evolution of an n-qubit system for time t within precision . If the Lindbladian consists of poly(n) operators that can each be expressed as a linear combination of poly(n) tensor products of Pauli operators then the gate cost of our algorithm is O(t polylog(t/)poly(n)). We also obtain similar bounds for the cases where the Lindbladian consists of local operators, and where the Lindbladian consists of sparse operators. This is remarkable in light of evidence that we provide indicating that the above efficiency is impossible to attain by first expressing Lindblad evolution as Schr¨odingerevolution on a larger system and tracing out the ancillary system: the cost of such a reduction incurs an efficiency overhead of O(t2/) even before the Hamiltonian evolution simulation begins. Instead, the approach of our algorithm is to use a novel variation of the \linear combinations of unitaries" construction that pertains to channels. 1 Introduction The problem of simulating the evolution of closed systems (captured by the Schr¨odingerequation) was proposed by Feynman [11] in 1982 as a motivation for building quantum computers. Since then, several quantum algorithms have appeared for this problem (see subsection 1.1 for references to these algorithms). However, many quantum systems of interest are not closed but are well-captured by the Lindblad Master equation [20, 12].
    [Show full text]
  • Arxiv:1611.07995V1 [Quant-Ph] 23 Nov 2016
    Factoring using 2n + 2 qubits with Toffoli based modular multiplication Thomas H¨aner∗ Station Q Quantum Architectures and Computation Group, Microsoft Research, Redmond, WA 98052, USA and Institute for Theoretical Physics, ETH Zurich, 8093 Zurich, Switzerland Martin Roettelery and Krysta M. Svorez Station Q Quantum Architectures and Computation Group, Microsoft Research, Redmond, WA 98052, USA We describe an implementation of Shor's quantum algorithm to factor n-bit integers using only 2n+2 qubits. In contrast to previous space-optimized implementations, ours features a purely Toffoli based modular multiplication circuit. The circuit depth and the overall gate count are in (n3) and (n3 log n), respectively. We thus achieve the same space and time costs as TakahashiO et al. [1], Owhile using a purely classical modular multiplication circuit. As a consequence, our approach evades most of the cost overheads originating from rotation synthesis and enables testing and localization of faults in both, the logical level circuit and an actual quantum hardware implementation. Our new (in-place) constant-adder, which is used to construct the modular multiplication circuit, uses only dirty ancilla qubits and features a circuit size and depth in (n log n) and (n), respectively. O O I. INTRODUCTION Cuccaro [5] Takahashi [6] Draper [4] Our adder Size Θ(n) Θ(n) Θ(n2) Θ(n log n) Quantum computers offer an exponential speedup over Depth Θ(n) Θ(n) Θ(n) Θ(n) their classical counterparts for solving certain problems, n the most famous of which is Shor's algorithm [2] for fac- Ancillas n+1 (clean) n (clean) 0 2 (dirty) toring a large number N | an algorithm that enables the breaking of many popular encryption schemes including Table I.
    [Show full text]
  • Hamiltonian Simulation with Nearly Optimal Dependence on All Parameters
    Hamiltonian simulation with nearly optimal dependence on all parameters Dominic W. Berry∗ Andrew M. Childsy Robin Kothariz We present an algorithm for sparse Hamiltonian simulation that has optimal dependence on all pa- rameters of interest (up to log factors). Previous algorithms had optimal or near-optimal scaling in some parameters at the cost of poor scaling in others. Hamiltonian simulation via quantum walk has optimal dependence on the sparsity d at the expense of poor scaling in the allowed error . In contrast, an ap- proach based on fractional-query simulation provides optimal scaling in at the expense of poor scaling in d. Here we combine the two approaches, achieving the best features of both. By implementing a linear combination of quantum walk steps with coefficients given by Bessel functions, our algorithm achieves near-linear scaling in τ := dkHkmaxt and sublogarithmic scaling in 1/. Our dependence on is optimal, and we prove a new lower bound ruling out sublinear dependence on τ. The problem of simulating the dynamics of quantum systems was the original motivation for quantum com- puters [14] and remains one of their major potential applications. The first explicit quantum simulation algorithm [16] gave a method for simulating Hamiltonians that are sums of local interaction terms. Aharonov and Ta-Shma gave an efficient simulation algorithm for the more general class of sparse Hamiltonians [2], and much subsequent work has given improved simulations [4,5,6,7,8, 12, 17, 18]. Sparse Hamiltonians include most physically realistic Hamiltonians as a special case. In addition, sparse Hamiltonian simulation can also be used to design other quantum algorithms [9, 10, 15].
    [Show full text]
  • Quantum Computing: Lecture Notes
    Quantum Computing: Lecture Notes Ronald de Wolf Preface These lecture notes were formed in small chunks during my “Quantum computing” course at the University of Amsterdam, Feb-May 2011, and compiled into one text thereafter. Each chapter was covered in a lecture of 2 45 minutes, with an additional 45-minute lecture for exercises and × homework. The first half of the course (Chapters 1–7) covers quantum algorithms, the second half covers quantum complexity (Chapters 8–9), stuff involving Alice and Bob (Chapters 10–13), and error-correction (Chapter 14). A 15th lecture about physical implementations and general outlook was more sketchy, and I didn’t write lecture notes for it. These chapters may also be read as a general introduction to the area of quantum computation and information from the perspective of a theoretical computer scientist. While I made an effort to make the text self-contained and consistent, it may still be somewhat rough around the edges; I hope to continue polishing and adding to it. Comments & constructive criticism are very welcome, and can be sent to [email protected] Attribution and acknowledgements Most of the material in Chapters 1–6 comes from the first chapter of my PhD thesis [71], with a number of additions: the lower bound for Simon, the Fourier transform, the geometric explanation of Grover. Chapter 7 is newly written for these notes, inspired by Santha’s survey [62]. Chapters 8 and 9 are largely new as well. Section 3 of Chapter 8, and most of Chapter 10 are taken (with many changes) from my “quantum proofs” survey paper with Andy Drucker [28].
    [Show full text]
  • Quantum Information Theory by Michael Aaron Nielsen
    Quantum Information Theory By Michael Aaron Nielsen B.Sc., University of Queensland, 1993 B.Sc. (First Class Honours), Mathematics, University of Queensland, 1994 M.Sc., Physics, University of Queensland, 1998 DISSERTATION Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Physics The University of New Mexico Albuquerque, New Mexico, USA December 1998 c 1998, Michael Aaron Nielsen ii Dedicated to the memory of Michael Gerard Kennedy 24 January 1951 – 3 June 1998 iii Acknowledgments It is a great pleasure to thank the many people who have contributed to this Dissertation. My deepest thanks goes to my friends and family, especially my parents, Howard and Wendy, for their support and encouragement. Warm thanks also to the many other people who have con- tributed to this Dissertation, especially Carl Caves, who has been a terrific mentor, colleague, and friend; to Gerard Milburn, who got me started in physics, in research, and in quantum in- formation; and to Ben Schumacher, whose boundless enthusiasm and encouragement has provided so much inspiration for my research. Throughout my graduate career I have had the pleasure of many enjoyable and helpful discussions with Howard Barnum, Ike Chuang, Chris Fuchs, Ray- mond Laflamme, Manny Knill, Mark Tracy, and Wojtek Zurek. In particular, Howard Barnum, Carl Caves, Chris Fuchs, Manny Knill, and Ben Schumacher helped me learn much of what I know about quantum operations, entropy, and distance measures for quantum information. The material reviewed in chapters 3 through 5 I learnt in no small measure from these people. Many other friends and colleagues have contributed to this Dissertation.
    [Show full text]
  • Andrew Childs University of Maryland Arxiv:1711.10980
    Toward the first quantum simulation with quantum speedup Andrew Childs University of Maryland Dmitri Maslov Yunseong Nam Neil Julien Ross Yuan Su arXiv:1711.10980 Simulating Hamiltonian dynamics on a small quantum computer Andrew Childs Department of Combinatorics & Optimization and Institute for Quantum Computing University of Waterloo based in part on joint work with Dominic Berry, Richard Cleve, Robin Kothari, and Rolando Somma Workshop on What do we do with a small quantum computer? IBM Watson, 9 December 2013 Toward practical quantum speedup IBM Google/UCSB Delft Maryland Important early goal: demonstrate quantum computational advantage … but can we find a practical application of near-term devices? Challenges • Improve experimental systems • Improve algorithms and their implementation, making the best use of available hardware Our goal: Produce concrete resource estimates for the simplest possible practical application of quantum computers Quantum simulation “… nature isn’t classical, dammit, A classical computer cannot even represent and if you want to make a the state efficiently. simulation of nature, you’d better make it quantum mechanical, and by golly it’s a wonderful problem, A quantum computer cannot produce a because it doesn’t look so easy.” complete description of the state. Richard Feynman (1981) Simulating physics with computers But given succinct descriptions of • the initial state (suitable for a quantum computer to prepare it efficiently) and Quantum simulation problem: Given a • a final measurement (say, measurements description of the Hamiltonian H, an of the individual qubits in some basis), evolution time t, and an initial state (0) , a quantum computer can efficiently answer produce the final state ( t ) (to within| i questions that (apparently) a classical one some error tolerance ²|) i cannot.
    [Show full text]
  • Exponential Improvement in Precision for Simulating Sparse Hamiltonians
    Exponential improvement in precision for simulating sparse Hamiltonians , , Dominic W. Berry∗ Andrew M. Childs† ‡ Richard Cleve§ ‡ Robin Kothari§ Rolando D. Somma¶ Abstract We provide a quantum algorithm for simulating the dynamics of sparse Hamiltonians with complexity sublogarithmic in the inverse error, an exponential improvement over previous meth- ods. Specifically, we show that a d-sparse Hamiltonian H acting on n qubits can be simulated 2 log(τ/ǫ) log (τ/ǫ) for time t with precision ǫ using O τ log log(τ/ǫ) queries and O τ log log(τ/ǫ) n additional 2-qubit 2 gates, where τ = d H maxt. Unlike previous approaches based on product formulas, the query complexity is independentk k of the number of qubits acted on, and for time-varying Hamiltonians, the gate complexity is logarithmic in the norm of the derivative of the Hamiltonian. Our al- gorithm is based on a significantly improved simulation of the continuous- and fractional-query models using discrete quantum queries, showing that the former models are not much more powerful than the discrete model even for very small error. We also simplify the analysis of this conversion, avoiding the need for a complex fault correction procedure. Our simplification relies on a new form of “oblivious amplitude amplification” that can be applied even though the reflection about the input state is unavailable. Finally, we prove new lower bounds showing that our algorithms are optimal as a function of the error. 1 Introduction Simulation of quantum mechanical systems is a major potential application of quantum computers. Indeed, the problem of simulating Hamiltonian dynamics was the original motivation for the idea of quantum computation [21].
    [Show full text]
  • Elementary Gates for Quantum Computation
    PHYSICAL REVIEW A VOLUME 52, NUMBER 5 NOVEMBER 1995 Elementary gates for quantum computation Adriano Barenco, ' Charles H. Bennett, '~ Richard Cleve, '~ David P. DiVincenzo, '~ Norman Margolus, '~ Peter Shor, Tycho Sleator, ~ John A. Smolin, and Harald Weinfurter 'Clarendon Laboratory, Oxford University, Oxford OXI 3PU, United Kingdom IBM Research, Yorktown Heights, ¹wYork 10598 Department of Computer Science, University of Calgary, Calgary, Alberta, Canada T2N IN4 Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139 ATckT Bell Laboratories, Murray Hill, New Jersey 07974 Physics Department, New York University, New York, New York 10003 Physics Department, University of California at Los Angeles, Los Angeles, California 90024 Institute for Experimental Physics, University of Innsbruck, A-6020 Innsbruck, Austria (Received 22 March 1995) We show that a set of gates that consists of all one-bit quantum gates [U(2)] and the two-bit exclusive-oR gate [that maps Boolean values (x,y) to (x,x&y)] is universal in the sense that all unitary operations on arbitrarily many bits n [U(2")] can be expressed as compositions of these gates. We investigate the number of the above gates required to implement other gates, such as generalized Deutsch-Toffoli gates, that apply a specific U(2) transformation to one input bit if and only if the logical AND of all remaining input bits is satisfied. These gates play a central role in many proposed constructions of quantum computational networks. We derive upper and lower bounds on the exact number of elementary gates required to build up a variety of two- and three-bit quantum gates, the asymptotic number required for n-bit Deutsch-Toffoli gates, and make some observations about the number required for arbitrary n-bit unitary operations.
    [Show full text]
  • Quantum Interpolation of Polynomials
    Quantum interpolation of polynomials Daniel M. Kane∗ Samuel A. Kutin† August 2009 Abstract We consider quantum interpolation of polynomials. We imagine a quantum com- puter with black-box access to input/output pairs (xi,f(xi)), where f is a degree-d polynomial, and we wish to compute f(0). We give asymptotically tight quantum lower bounds for this problem, even in the case where 0 is among the possible values of xi. 1 Introduction Can a quantum computer efficiently interpolate polynomials? Can it distinguish low-degree from high-degree polynomials? We consider black-box algorithms that seek to learn infor- mation about a polynomial f from input/output pairs (xi, f(xi)). We define a more general class of (d,S)-independent function properties, where, outside of a set S of exceptions, know- ing d input values does not help one predict the answer. There are essentially two strategies to computing such a function: query d + 1 random input values, or search for one of the S | | exceptions. We show that, up to constant factors, we cannot beat these two approaches. Let be a collection of functions from some domain D to some range R. A property F is a (nontrivial) map : 0, 1 . We say that is (d,S)-independent for some subset P F →{ } P S D if, for any z , ..., zd D with z , ..., zr S and zr , ..., zd / S (where ⊆ 1 ∈ 1 ∈ +1 ∈ r = zi S ), the (d r)-tuple of values (f(zr ),...,f(zd)) is independent of the (r + 1)- |{ } ∩ | − +1 tuple (f(z1),...,f(zr), (f)).
    [Show full text]
  • Classical and Quantum Algorithms for Finding Cycles
    City University of New York (CUNY) CUNY Academic Works Publications and Research CUNY Graduate Center 2006 Classical and Quantum Algorithms for Finding Cycles Jill Cirasella CUNY Graduate Center How does access to this work benefit ou?y Let us know! More information about this work at: https://academicworks.cuny.edu/gc_pubs/169 Discover additional works at: https://academicworks.cuny.edu This work is made publicly available by the City University of New York (CUNY). Contact: [email protected] Classical and Quantum Algorithms for Finding Cycles MSc Thesis (Afstudeerscriptie) written by Jill Cirasella (born in 1976 in New Jersey, United States) under the supervision of Prof. Dr. Harry Buhrman, and submitted to the Board of Examiners in partial fulfillment of the requirements for the degree of MSc in Logic at the Universiteit van Amsterdam. Date of the public defense: Members of the Thesis Committee: January 26, 2006 Prof. Dr. Harry Buhrman Dr. Benedikt L¨owe Dr. Ronald de Wolf Abstract Quantum computing—so weird, so wonderful—inspires much speculation about the line be- tween the possible and the impossible. (Of course, there is still unclarity about how “impossi- ble” intractable problems are and about how “possible” quantum computers are.) This thesis takes a slightly different tack: instead of focusing on how to make the impossible possible, it focuses on how to make the possible easier. More specifically, this paper discusses quantum algorithms for finding cycles in graphs, a problem for which polynomial-time classical algorithms already exist. It explains and com- pares the classical and quantum algorithms, and it introduces a few new algorithms and observations.
    [Show full text]