DOCSLIB.ORG

The Hidden Subgroup Problem for Generalized Quaternions (QHSP) Is Defined As Follows

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
The Hidden Subgroup Problem for Generalized Quaternions (QHSP) Is Defined As Follows THE HIDDEN SUBGROUP PROBLEM FOR GENERALIZED QUATERNIONS by JULIA TUMASOVA UPTON A DISSERTATION Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Mathematics in the Graduate School of The University of Alabama TUSCALOOSA, ALABAMA 2009 Copyright Julia Tumasova Upton 2009 ALL RIGHTS RESERVED ABSTRACT The hidden subgroup problem is a pivotal problem in quantum computation since it reflects the structure of tasks for which quantum algorithms significantly outperform classical algorithms. In this dissertation, a quantum algorithm that solves the hidden subgroup problem over the general- ized quaternion group is developed. The algorithm employs the abelian quantum Fourier transform and Kuperberg sieve to reveal the hidden subgroup. ii DEDICATION I dedicate this dissertation to my grandmother, Tamara Semenovna Babakhanova, an elec- trical engineer extraordinaire, who showered me with love and inspired my interest in mathematics and science. I love you. I miss you. I also dedicate this work to my husband Mark, who supported me through every step of this journey. Without you, my love, this would not have been possible. iii ACKNOWLEDGMENTS I would like to thank, first and foremost, my adviser, Dr. Jon Corson, who enthusiastically ventured with me into the exciting new world of quantum computation. Our many discussions of mathematics, physics, technology, and the human condition inspired and guided me throughout this undertaking. I also thank my committee members, Dr. Joseph Neggers, Dr. Martin Evans, Dr. Martyn Dixon, and Dr. Allen Stern for their support. I am indebted to my family for the constant encouragement, and the sacrifices made to help me succeed in this endeavor. I thank my husband Mark for the inspiration I drew from our numerous discussions and for being there when I needed him most. I am grateful to my daughter Sophia for bringing such joy into my life and making it all worthwhile. iv CONTENTS ABSTRACT . ii DEDICATION . iii ACKNOWLEDGMENTS . iv LIST OF TABLES . vii LIST OF FIGURES . viii 1 INTRODUCTION . 1 1.1 The Power of A Quantum Computer . 1 1.2 A Brief History of Quantum Computing . 3 1.3 The Hidden Subgroup Problem in Quantum Computing . 4 1.3.1 General Formulation and Special Cases . 4 1.3.2 The Dihedral Hidden Subgroup Problem . 5 2 A QUANTUM MODEL OF COMPUTATION . 6 2.1 The Quantum Mechanical Framework . 6 2.2 The No-Cloning Theorem . 11 2.3 Quantum Gates . 11 2.3.1 Single-qubit gates . 12 2.3.2 Multi-qubit gates . 16 2.3.3 Universal Quantum Gates . 20 2.4 Probabilistic Nature of Quantum Algorithms . 21 3 SHOR’S FACTORIZATION ALGORITHM . 24 v 3.1 Reduction of Factorization to Order Finding . 24 3.2 The Quantum Part: Order Finding . 25 3.2.1 The Main Ingredient: Quantum Fourier Transform . 25 3.2.2 Quantum Algorithm for Order Finding . 29 3.3 Shor’s Algorithm Applied: An Illustration . 31 3.4 Shor’s Algorithm as a Special Case of the Abelian HSP . 35 4 HIDDEN SUBGROUP PROBLEM FOR QUATERNIONS . 37 4.1 Tablets From On High: The Story of Quaternions . 37 4.1.1 The Origins . 37 4.1.2 The Struggle . 42 4.1.3 The Revival . 43 4.2 The Group Q4 ..................................... 44 4.3 The Group Q8 ..................................... 46 4.3.1 Group Presentation . 46 4.3.2 Subgroup Classification . 48 4.3.3 Group Construction . 49 4.4 The Generalized Quaternions . 52 4.4.1 Group Presentation and Construction . 52 4.4.2 Subgroup Structure . 55 4.5 Hidden Subgroup Algorithm for Generalized Quaternions . 56 4.5.1 Hidden Subgroup Problem for Generalized Quaternions . 56 4.5.2 Coset Sample Generation Procedure . 58 4.5.3 The Kuperberg Sieve . 60 REFERENCES . 64 vi LIST OF TABLES 1.1 Special Cases of The Hidden Subgroup Problem . 4 2.1 The AND Operation . 19 1 2.2 The Chernoff bounds for d = 4 ............................ 23 3.1 Registers’ States . 32 4.1 Cayley Table for Q8 .................................. 47 4.2 Multiplication Table for Q8 .............................. 51 4.3 “Twisted” Multiplication . 52 vii LIST OF FIGURES 2.1 A Simple Quantum Circuit . 14 2.2 Hadamard Gates in Parallel . 15 2.3 CNOT Gate . 16 2.4 The Toffoli Gate . 19 3.1 Quantum Circuit for QFT † .............................. 29 3.2 Quantum Circuit for Period Finding . 29 3.3 Probability Plot . 33 3.4 Shor’s Algorithm as a Case of HSP . 35 3.5 Implementable Shor’s Algorithm as a Case of HSP . 36 4.1 Quaternion Plaque on Broom Bridge in Dublin . 39 4.2 Quantum Circuit for Period Finding over Q4 ..................... 44 4.3 Reduction of QHSP . 57 viii CHAPTER 1 INTRODUCTION Reality is that which, when you stop believing in it, does not go away. Philip K. Dick 1.1 The Power of A Quantum Computer A quantum computer is a computing device whose operation is based on the uniquely quantum-mechanical effects of interference and entanglement. The quantum bits (qubits), named after a cubit, an ancient unit of length, are prepared in a superposition of classical computation states which are combined to interfere producing the computation output. Qubits are manipulated by quantum gates, most of which represent unitary transformations. The general state of a single qubit is a unit vector in a two-dimensional Hilbert space and with a standard choice of the computational basis fj0i;j1ig can be written as 0 1 a B C jyi = a j0i + b j1i = @ A; (1.1) b where amplitudes a and b are complex numbers such that jaj2 + jbj2 = 1 (1.2) The state jyi is in a superposition of the states j0i and j1i, i.e., it exists in both states simultane- ously. 1 The quantum state for a system of n qubits is a unit vector in a 2n-dimensional Hilbert space and can be expressed in the computational basis as 1 1 (n) jy i = ∑ ::: ∑ ai0;:::;in−1 ji0 :::in−1i (1.3) i0=0 in−1=0 where jy(n)i is normalized. A system of n qubits represents a superposition of 2n states, allowing a quantum computer to operate on states simultaneously. This phenomenon of quantum parallelism is the source of the quantum computer’s overwhelming power that allows for the execution of algorithms which no classical computer can efficiently handle. However, the only way to extract information from qubits is to subject them to measurement, which reduces the system to a single state in accordance with the Born rule. The rule states that for a qubit in a superposition (1.1) the measurement result is 0 with probability jaj2 or 1 with probability jbj2. Thus, the post-measurement state no longer contains any information pertaining to the amplitudes of the pre-measurement superposition state, other than indicating that the particular amplitude corresponding to the actual output state was not 0, and likely not exceedingly small. Moreover, a measurement gate is not unitary, i.e., it is irreversible. For an input x covering the range 0 ≤ x < 2n and a computed function f , the measurement of the input register produces a single randomly chosen x0, while the measurement of the output register gives a single corresponding value f (x0). To take advantage of superposition and thus achieve an exponential speedup over the clas- sical computer, quantum algorithms involve judicial arrangements of unitary gates, in some cases supplemented by intermediate measurement gates acting on subsets of a system’s qubits. In most cases, this approach leads to an extraction of information about global properties of f , which man- ifest themselves in the relations between the values of f for many distinct values of x, information that a classical computer can only produce by making many independent evaluations. 2 1.2 A Brief History of Quantum Computing The Turing machine is the standard mathematical model of a classical computer. A math- ematical model of a quantum computer, just as independent of a physical realization, was first discussed by Manin [1] in 1980. A few years later, Feynman [2, 3] pointed out that simulation of certain quantum systems on a classical computer requires time that scales exponentially with the number of particles in a simulated system. Feynman proposed that a quantum system might be efficiently simulated by a computer that exploits quantum effects. In 1985, a concrete mathematical model, the quantum Turing machine, was introduced by Deutsch [4], who followed it up in 1989 [5] by an equivalent but more convenient model, the quantum circuit. These ideas led to the search for further applications of a quantum computer, initially resulting in solutions to somewhat contrived problems, e.g., the Deutsch-Jozsa algorithm [6], the Simon algorithm [7], and the Bernstein-Varzani algorithm [8]. Nonetheless, these early results demonstrated the existence of specific problems that can be handled dramatically faster by a quantum computer. A breakthrough that elevated quantum computation from a primarily academic exercise to a matter of great practical significance came in 1994 when Shor [9] introduced polynomial- time quantum algorithms for factoring numbers and computing the discrete logarithm. The widely used public key cryptography systems RSA and Diffie-Hellman rely on the fact that there are no polynomial-time classical algorithms for these problems. When implemented on a quantum computer, Shor’s algorithms will break these encryption schemes in a matter of seconds. Another masterpiece of quantum-computational software, Grover’s algorithm [10], was introduced in 1996, offering a solution for unstructured search problems with quadratic speedup over classical search algorithms. Recently Hallgren [11] developed a quantum algorithm for Pell’s equation with exponential speedup over any existing classical algorithm. Quantum algorithms that outperform classical algorithms can be separated into three cate- gories [12]: 1.
Load more

Recommended publications
  • A Note on the Security of CSIDH
    A note on the security of CSIDH Jean-Fran¸cois Biasse1, Annamaria Iezzi1, and Michael J. Jacobson, Jr.2 1 Department of Mathematics and Statistics University of South Florida biasse,aiezzi @usf.edu 2 { } Department of Computer Science University of Calgary [email protected] Abstract. We propose an algorithm for computing an isogeny between two elliptic curves E1,E2 defined over a finite field such that there is an imaginary quadratic order satisfying End(Ei) for i = 1, 2. This O O ≃ concerns ordinary curves and supersingular curves defined over Fp (the latter used in the recent CSIDH proposal). Our algorithm has heuris- tic asymptotic run time eO√log(|∆|) and requires polynomial quantum memory and eO√log(|∆|) classical memory, where ∆ is the discriminant of . This asymptotic complexity outperforms all other available method O for computing isogenies. We also show that a variant of our method has asymptotic run time ˜ eO√log(|∆|) while requesting only polynomial memory (both quantum and classical). 1 Introduction Given two elliptic curves E1, E2 defined over a finite field Fq, the isogeny prob- lem is to compute an isogeny φ : E E , i.e. a non-constant morphism that 1 → 2 maps the identity point on E1 to the identity point on E2. There are two dif- ferent types of elliptic curves: ordinary and supersingular. The latter have very particular properties that impact the resolution of the isogeny problem. The first instance of a cryptosystem based on the hardness of computing isogenies arXiv:1806.03656v4 [cs.CR] 1 Aug 2018 was due to Couveignes [12], and its concept was independently rediscovered by Stolbunov [31].
    [Show full text]
  • Ph 219B/CS 219B
    1 Ph 219b/CS 219b Exercises Due: Wednesday 22 February 2006 6.1 Estimating the trace of a unitary matrix Recall that using an oracle that applies the conditional unitary Λ(U), Λ(U): |0i⊗|ψi7→|0i⊗|ψi , |1i⊗|ψi7→|1i⊗U|ψi (1) (where U is a unitary transformation acting on n qubits), we can measure the eigenvalues of U. If the state |ψi is the eigenstate |λi of U with eigenvalue λ = exp(2πiφ), then√ by querying the oracle k times, we can determine φ to accuracy O(1/ k). But suppose that we replace the pure state |ψi in eq. (1) by the max- imally mixed state of n qubits, ρ = I/2n. a) Show that, with k queries, we can estimate both the real part and n the imaginary√ part of tr (U) /2 , the normalized trace of U,to accuracy O(1/ k). b) Given a polynomial-size quantum circuit, the problem of estimat- ing to fixed accuracy the normalized trace of the unitary trans- formation realized by the circuit is believed to be a hard problem classically. Explain how this problem can be solved efficiently with a quantum computer. The initial state needed for each query consists of one qubit in the pure state |0i and n qubits in the maximally mixed state. Surprisingly, then, the initial state of the computer that we require to run this (apparently) powerful quantum algorithm contains only a constant number of “clean” qubits, and O(n) very noisy qubits. 6.2 A generalization of Simon’s problem n Simon’s problem is a hidden subgroup problem with G = Z2 and k H = Z2 = {0,a}.
    [Show full text]
  • Hidden Shift Attacks on Isogeny-Based Protocols
    One-way functions and malleability oracles: Hidden shift attacks on isogeny-based protocols P´eterKutas1, Simon-Philipp Merz2, Christophe Petit3;1, and Charlotte Weitk¨amper1 1 University of Birmingham, UK 2 Royal Holloway, University of London, UK 3 Universit´elibre de Bruxelles, Belgium Abstract. Supersingular isogeny Diffie-Hellman key exchange (SIDH) is a post-quantum protocol based on the presumed hardness of com- puting an isogeny between two supersingular elliptic curves given some additional torsion point information. Unlike other isogeny-based proto- cols, SIDH has been widely believed to be immune to subexponential quantum attacks because of the non-commutative structure of the endo- morphism rings of supersingular curves. We contradict this commonly believed misconception in this paper. More precisely, we highlight the existence of an abelian group action on the SIDH key space, and we show that for sufficiently unbalanced and over- stretched SIDH parameters, this action can be efficiently computed (heuris- tically) using the torsion point information revealed in the protocol. This reduces the underlying hardness assumption to a hidden shift problem instance which can be solved in quantum subexponential time. We formulate our attack in a new framework allowing the inversion of one-way functions in quantum subexponential time provided a malleabil- ity oracle with respect to some commutative group action. This frame- work unifies our new attack with earlier subexponential quantum attacks on isogeny-based protocols, and it may be of further interest for crypt- analysis. 1 Introduction The hardness of solving mathematical problems such as integer factorization or the computation of discrete logarithms in finite fields and elliptic curve groups guarantees the security of most currently deployed cryptographic protocols.
    [Show full text]
  • Arxiv:1805.04179V2
    The Hidden Subgroup Problem and Post-quantum Group-based Cryptography Kelsey Horan1 and Delaram Kahrobaei2,3 1 The Graduate Center, CUNY, USA [email protected] 2 The Graduate Center and NYCCT, CUNY 3 New York University, USA [email protected] ⋆ Abstract. In this paper we discuss the Hidden Subgroup Problem (HSP) in relation to post-quantum cryptography. We review the relationship between HSP and other computational problems discuss an optimal so- lution method, and review the known results about the quantum com- plexity of HSP. We also overview some platforms for group-based cryp- tosystems. Notably, efficient algorithms for solving HSP in the proposed infinite group platforms are not yet known. Keywords: Hidden Subgroup Problem, Quantum Computation, Post-Quantum Cryptography, Group-based Cryptography 1 Introduction In August 2015 the National Security Agency (NSA) announced plans to upgrade security standards; the goal is to replace all deployed cryptographic protocols with quantum secure protocols. This transition requires a new security standard to be accepted by the National Institute of Standards and Technology (NIST). Proposals for quantum secure cryptosystems and protocols have been submitted for the standardization process. There are six main primitives currently pro- posed to be quantum-safe: (1) lattice-based (2) code-based (3) isogeny-based (4) multivariate-based (5) hash-based, and (6) group-based cryptographic schemes. arXiv:1805.04179v2 [cs.CR] 21 May 2018 One goal of cryptography, as it relates to complexity theory, is to analyze the complexity assumptions used as the basis for various cryptographic protocols and schemes. A central question is determining how to generate intractible instances of these problems upon which to implement an actual cryptographic scheme.
    [Show full text]
  • Fully Graphical Treatment of the Quantum Algorithm for the Hidden Subgroup Problem
    FULLY GRAPHICAL TREATMENT OF THE QUANTUM ALGORITHM FOR THE HIDDEN SUBGROUP PROBLEM STEFANO GOGIOSO AND ALEKS KISSINGER Quantum Group, University of Oxford, UK iCIS, Radboud University. Nijmegen, Netherlands Abstract. The abelian Hidden Subgroup Problem (HSP) is extremely general, and many problems with known quantum exponential speed-up (such as integers factorisation, the discrete logarithm and Simon's problem) can be seen as specific instances of it. The traditional presentation of the quantum protocol for the abelian HSP is low-level, and relies heavily on the the interplay between classical group theory and complex vector spaces. Instead, we give a high-level diagrammatic presentation which showcases the quantum structures truly at play. Specifically, we provide the first fully diagrammatic proof of correctness for the abelian HSP protocol, showing that strongly complementary observables are the key ingredient to its success. Being fully diagrammatic, our proof extends beyond the traditional case of finite-dimensional quantum theory: for example, we can use it to show that Simon's problem can be efficiently solved in real quantum theory, and to obtain a protocol that solves the HSP for certain infinite abelian groups. 1. Introduction The advent of quantum computing promises to solve a number number of problems which have until now proven intractable for classical computers. Amongst these, one of the most famous is Shor's algorithm [Sho95, EJ96]: it allows for an efficient solution of the integer factorisation problem and the discrete logarithm problem, the hardness of which underlies many of the cryptographic algorithms which we currently entrust with our digital security (such as RSA and DHKE).
    [Show full text]
  • Quantum Factoring, Discrete Logarithms, and the Hidden Subgroup Problem
    Q UANTUM C OMPUTATION QUANTUM FACTORING, DISCRETE LOGARITHMS, AND THE HIDDEN SUBGROUP PROBLEM Among the most remarkable successes of quantum computation are Shor’s efficient quantum algorithms for the computational tasks of integer factorization and the evaluation of discrete logarithms. This article reviews the essential ingredients of these algorithms and draws out the unifying generalization of the so-called hidden subgroup problem. uantum algorithms exploit quantum- these issues, including further applications such physical effects to provide new modes as the evaluation of discrete logarithms. I will of computation that are not available outline a unifying generalization of these ideas: Qto “conventional” (classical) comput- the so-called hidden subgroup problem, which is ers. In some cases, these modes provide efficient just a natural group-theoretic generalization of algorithms for computational tasks for which no the periodicity determination problem. Finally, efficient classical algorithm is known. The most we’ll examine some interesting open questions celebrated quantum algorithm to date is Shor’s related to the hidden subgroup problem for non- algorithm for integer factorization.1–3 It provides commutative groups, where future quantum al- a method for factoring any integer of n digits in gorithms might have a substantial impact. an amount of time (for example, in a number of computational steps), whose length grows less rapidly than O(n3). Thus, it is a polynomial time Periodicity algorithm in contrast to the best-known classi- Think of periodicity determination as a par- cal algorithm for this fundamental problem, ticular kind of pattern recognition. Quantum which runs in superpolynomial time of order computers can store and process a large volume exp(n1/3(log n)2/3).
    [Show full text]
  • The Hidden Subgroup Problem for Infinite Groups
    The hidden subgroup problem for infinite groups Greg Kuperberg, UC Davis We consider the hidden subgroup problem for infinite groups, beyond the celebrated original cases estab- lished by Shor and Kitaev. 1 Motivation and main results Some of the most important quantum algorithms solve rigorously stated computational problems and are superpolynomially faster than classical alternatives. This includes Shor’s algorithm for period finding [13]. The hidden subgroup problem is a popular framework for many such algorithms [10]. If G is a discrete group and X is an unstructured set, a function f : G ! X hides a subgroup H ≤ G means that f (x) = f (y) if and only if y = xh for some h 2 H. In other words, f is a hiding function for the hidden subgroup H when it is right H-periodic and otherwise injective, as summarized in this commutative diagram: f G X G=H The hidden subgroup problem (HSP) is then the problem of calculating H from arithmetic in G and efficient access to f . The computational complexity of HSP depends greatly on the ambient group G, as well as other criteria such as that H is normal or lies in a specific conjugacy class of subgroups. The happiest cases of HSP are those that both have an efficient quantum algorithm, and an unconditional proof that HSP is classically intractable when f is given by an oracle. Shor’s algorithm solves HSP when G = Z, the group of integers under addition. Even though Z is infinite and Shor’s algorithm is a major motivation for HSP, many of the results since then have been about HSP for finite groups [2, Ch.
    [Show full text]
  • THE HIDDEN SUBGROUP PROBLEM by ANALES DEBHAUMIK A
    THE HIDDEN SUBGROUP PROBLEM By ANALES DEBHAUMIK A DISSERTATION PRESENTED TO THE GRADUATE SCHOOL OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY UNIVERSITY OF FLORIDA 2010 c 2010 Anales Debhaumik 2 I dedicate this to everyone who helped me in my research. 3 ACKNOWLEDGMENTS First and foremost I would like to express my sincerest gratitude to my advisor Dr Alexandre Turull. Without his guidance and persistent help this thesis would not have been possible. I would also like to thank sincerely my committee members for their help and constant encouragement. Finally I would like to thank my wife Manisha Brahmachary for her love and constant support. 4 TABLE OF CONTENTS page ACKNOWLEDGMENTS .................................. 4 ABSTRACT ......................................... 6 CHAPTER 1 INTRODUCTION ................................... 8 1.1 Preliminaries .................................. 8 1.2 Quantum Computing .............................. 11 1.3 Quantum Fourier Transform .......................... 12 1.4 Hidden Subgroup Problem .......................... 14 1.5 Distinguishability ................................ 18 2 HIDDEN SUBGROUPS FOR ALMOST ABELIAN GROUPS ........... 20 2.1 Algorithm to find the hidden subgroups .................... 20 2.2 An Almost Abelian Group of Order 3 2n ................... 22 · 3 KEMPE-SHALEV DISTINGUISHABLITY ..................... 33 4 ALGORITHMIC DISTINGUISHABILITY ...................... 43 4.1 An algorithm for distinguishability ......................
    [Show full text]
  • Quantum Algorithm for a Generalized Hidden Shift Problem
    Quantum algorithm for a generalized hidden shift problem Andrew Childs Wim van Dam Caltech UC Santa Barbara What is the power of quantum computers? Quantum mechanical computers can efficiently solve problems that classical computers (apparently) cannot. • Manin/Feynman, early 1980s: Simulating quantum systems • Deutsch 1985, Deutsch-Jozsa 1992, Bernstein-Vazirani 1993, Simon 1994: Black box problems • Shor 1994: Factoring, discrete logarithm • Many authors, late 1990s–Present: Some nonabelian hidden subgroup problems • Freedman-Kitaev-Larsen 2000: Approximating Jones polynomial • Hallgren 2002: Pell’s equation • van Dam-Hallgren-Ip 2002: Some hidden shift problems (e.g., shifted Legendre symbol) • van Dam-Seroussi 2002: Estimating Gauss/Jacobi sums • Childs, Cleve, Deotto, Farhi, Gutmann, Spielman 2003: Black box graph traversal • van Dam 2004, Kedlaya 2004: Approximately counting solutions of polynomial equations • Hallgren 2005, Schmidt-Vollmer 2005: Finding unit/class groups of number fields What is the power of quantum computers? Quantum mechanical computers can efficiently solve problems that classical computers (apparently) cannot. • Manin/Feynman, early 1980s: Simulating quantum systems • Deutsch 1985, Deutsch-Jozsa 1992, Bernstein-Vazirani 1993, Simon 1994: Black box problems • Shor 1994: Factoring, discrete logarithm • Many authors, late 1990s–Present: Some nonabelian hidden subgroup problems • Freedman-Kitaev-Larsen 2000: Approximating Jones polynomial • Hallgren 2002: Pell’s equation • van Dam-Hallgren-Ip 2002: Some hidden shift problems (e.g., shifted Legendre symbol) • van Dam-Seroussi 2002: Estimating Gauss/Jacobi sums • Childs, Cleve, Deotto, Farhi, Gutmann, Spielman 2003: Black box graph traversal • van Dam 2004, Kedlaya 2004: Approximately counting solutions of polynomial equations • Hallgren 2005, Schmidt-Vollmer 2005: Finding unit/class groups of number fields Questions: What is the power of quantum computers? Quantum mechanical computers can efficiently solve problems that classical computers (apparently) cannot.
    [Show full text]
  • Generic Quantum Fourier Transforms
    GENERIC QUANTUM FOURIER TRANSFORMS Cristopher Moore Daniel Rockmore University of New Mexico Dartmouth College [email protected] [email protected] Alexander Russell University of Connecticut [email protected] Abstract problems over Abelian groups are well-understood, for The quantum Fourier transform (QFT) is the principal non-Abelian groups our understanding of these problems ingredient of most efficient quantum algorithms. We remains embarrassingly sporadic. Aside from their natu- present a generic framework for the construction of ral appeal, these lines of research are motivated by their efficient quantum circuits for the QFT by “quantizing” direct relationship to the graph isomorphism problem: an the highly successful separation of variables technique efficient solution to the hidden subgroup problem over the for the construction of efficient classical Fourier trans- (non-Abelian) symmetric groups would yield an efficient forms. Specifically, we apply the existence of computable quantum algorithm for graph isomorphism. Bratteli diagrams, adapted factorizations, and Gel’fand- Over the cyclic group Zn, the quantum Fourier Tsetlin bases to provide efficient quantum circuits for the transform refers to the transformation taking the state ∑ f (z)|zi to the state ∑ω fˆ(ω)|ωi, where QFT over a wide variety of finite Abelian and non-Abelian z∈Zn ∈Zn ˆ ω groups, including all group families for which efficient f : Zn → C is a function with k f k2 = 1 and f ( ) = 2πiωz/n QFTs are currently known and many new group families. ∑z f (z)e denotes the familiar discrete Fourier trans- Moreover, our method provides the first subexponential- form at the frequency ω.
    [Show full text]
  • Arxiv:Quant-Ph/0603140V1 15 Mar 2006
    IS GROVER’S ALGORITHM A QUANTUM HIDDEN SUBGROUP ALGORITHM ? SAMUEL J. LOMONACO, JR. AND LOUIS H. KAUFFMAN Abstract. The arguments given in this paper suggest that Grover’s and Shor’s algorithms are more closely related than one might at first expect. Specifically, we show that Grover’s algorithm can be viewed as a quantum al- gorithm which solves a non-abelian hidden subgroup problem (HSP). But we then go on to show that the standard non-abelian quantum hidden subgroup (QHS) algorithm can not find a solution to this particular HSP. This leaves open the question as to whether or not there is some mod- ification of the standard non-abelian QHS algorithm which is equivalent to Grover’s algorithm. Contents 1. Introduction 1 2. Definition of the hidden subgroup problem (HSP) and hidden subgroup algorithms 2 3. The generic QHS algorithm QRand 3 4. Pushing HSPs for the generic QHS algorithm QRand 4 5. Shor’s algorithm 5 6. Description of Grover’s algorithm 6 7. The symmetry hidden within Grover’s algorithm 7 8. A comparison of Grover’s and Shor’s algorithms 9 9. However 10 10. Conclusions and Open Questions 11 References 12 arXiv:quant-ph/0603140v1 15 Mar 2006 1. Introduction Is Grover’s algorithm a quantum hidden subgroup (QHS) algorithm ? We do not completely answer this question. Instead, we show that Grover’s algorithm is a QHS algorithm in the sense that it can be rephrased as a quantum algorithm which solves a non-abelian hidden subgroup problem (HSP) on the sym- metric group SN .
    [Show full text]
  • The Hidden Subgroup Problem for Infinite Groups
    Shor HSP Complexity Rationals Free groups Lattices Epilogue The hidden subgroup problem for infinite groups Greg Kuperberg UC Davis October 29, 2020 (Paper in preparation.) 1/25 Shor HSP Complexity Rationals Free groups Lattices Epilogue Shor's algorithm Let f : Z ! X be a function to a set X such that: • We can compute f in polynomial time. • f (x + h) = f (x) for an unknown period h. • f (x) 6= f (y) when h 6 j x − y. Finding h from f is the hidden period problem, or the hidden subgroup problem for the integers Z. Theorem (Shor) A quantum computer can solve the hidden period problem in time poly(logh). I.e., in quantum polynomialp time in khkbit. Note: If f is black box, then this takes Ω(e h) = exp(Ω(khkbit)) classical queries. 2/25 Shor HSP Complexity Rationals Free groups Lattices Epilogue Factoring integers Corollary (Shor) Integers can be factored in quantum polynomial time. Suppose that N is odd and not a prime power. Shor's algorithm × reveals the order ord(a) of a prime residue a 2 (Z=N) via x f (x) = a 2 Z=N: If a is random, then ord(a) is even and b = aord(a)=2 6= ±1 with good odds, whence Njb2 − 1 = (b + 1)(b − 1) N 6 j b ± 1 yields a factor of N. 3/25 Shor HSP Complexity Rationals Free groups Lattices Epilogue Shor-Kitaev k In a second example of HSP, let f : Z ! X be periodic with k respect to a finite-index sublattice H ≤ Z .
    [Show full text]