DOCSLIB.ORG

Chapter 6 Quantum Computation

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Chapter 6 Quantum Computation Chapter 6 Quantum Computation 6.1 Classical Circuits The concept of a quantum computer was introduced in Chapter 1. Here we will specify our model of quantum computation more precisely, and we will point out some basic properties of the model. But before we explain what a quantum computer does, perhaps we should say what a classical computer does. 6.1.1 Universal gates A classical (deterministic) computer evaluates a function: given n-bits of input it produces m-bits of output that are uniquely determined by the input; that is, it finds the value of f : 0, 1 n 0, 1 m, (6.1) { } →{ } for a particular specified n-bit argument. A function with an m-bit value is equivalent to m functions, each with a one-bit value, so we may just as well say that the basic task performed by a computer is the evaluation of f : 0, 1 n 0, 1 . (6.2) { } →{ } We can easily count the number of such functions. There are 2n possible inputs, and for each input there are two possible outputs. So there are n altogether 22 functions taking n bits to one bit. 1 2 CHAPTER 6. QUANTUM COMPUTATION The evaluation of any such function can be reduced to a sequence of elementary logical operations. Let us divide the possible values of the input x = x1x2x3 ...xn, (6.3) into one set of values for which f(x) = 1, and a complementary set for which f(x) = 0. For each x(a) such that f(x(a)) = 1, consider the function f (a) such that 1 x = x(a) f (a)(x)= (6.4) ( 0 otherwise Then f(x)= f (1)(x) f (2)(x) f (3)(x) ... (6.5) ∨ ∨ ∨ f is the logical OR ( ) of all the f (a)’s. In binary arithmetic the operation of two bits may be represented∨ ∨ x y = x + y x y; (6.6) ∨ − · it has the value 0 if x and y are both zero, and the value 1 otherwise. Now consider the evaluation of f (a). In the case where x(a) = 111 ... 1, we may write f (a)(x)= x x x ... x ; (6.7) 1 ∧ 2 ∧ 3 ∧ n it is the logical AND ( ) of all n bits. In binary arithmetic, the AND is the product ∧ x y = x y. (6.8) ∧ · For any other x(a), f (a) is again obtained as the AND of n bits, but where the NOT ( ) operation is first applied to each x such that x(a) = 0; for example ¬ i i f (a)(x)=( x ) x x ( x ) ... (6.9) ¬ 1 ∧ 2 ∧ 3 ∧ ¬ 4 ∧ if x(a) = 0110 ... (6.10) 6.1. CLASSICAL CIRCUITS 3 The NOT operation is represented in binary arithmetic as x = 1 x. (6.11) ¬ − We have now constructed the function f(x) from three elementary logi- cal connectives: NOT, AND, OR. The expression we obtained is called the “disjunctive normal form” of f(x). We have also implicitly used another operation, COPY, that takes one bit to two bits: COPY : x xx. (6.12) → We need the COPY operation because each f (a) in the disjunctive normal form expansion of f requires its own copy of x to act on. In fact, we can pare our set of elementary logical connectives to a smaller set. Let us define a NAND (“NOT AND”) operation by x y = (x y)=( x) ( y). (6.13) ↑ ¬ ∧ ¬ ∨ ¬ In binary arithmetic, the NAND operation is x y = 1 xy. (6.14) ↑ − If we can COPY, we can use NAND to perform NOT: x x = 1 x2 = 1 x = x. (6.15) ↑ − − ¬ (Alternatively, if we can prepare the constant y = 1, then x 1 = 1 x = x.) Also, ↑ − ¬ (x y) (x y)= (x y) = 1 (1 xy)= xy = x y, ↑ ↑ ↑ ¬ ↑ − − ∧ (6.16) and (x x) (y y)=( x) ( y) = 1 (1 x)(1 y) ↑ ↑ ↑ ¬ ↑ ¬ − − − = x + y xy = x y. (6.17) − ∨ So if we can COPY, NAND performs AND and OR as well. We conclude that the single logical connective NAND, together with COPY, suffices to evaluate any function f. (You can check that an alternative possible choice of the universal connective is NOR: x y = (x y)=( x) ( y).) (6.18) ↓ ¬ ∨ ¬ ∧ ¬ 4 CHAPTER 6. QUANTUM COMPUTATION If we are able to prepare a constant bit (x =0 or x = 1), we can reduce the number of elementary operations from two to one. The NAND/NOT gate (x,y) (1 x, 1 xy), (6.19) → − − computes NAND (if we ignore the first output bit) and performs copy (if we set the second input bit to y = 1, and we subsequently apply NOT to both output bits). We say, therefore, that NAND/NOT is a universal gate. If we have a supply of constant bits, and we can apply the NAND/NOT gates to any chosen pair of input bits, then we can perform a sequence of NAND/NOT gates to evaluate any function f : 0, 1 n 0, 1 for any { } → { } value of the input x = x1x2 ...xn. These considerations motivate the circuit model of computation. A com- puter has a few basic components that can perform elementary operations on bits or pairs of bits, such as COPY, NOT, AND, OR. It can also prepare a constant bit or input a variable bit. A computation is a finite sequence of such operations, a circuit, applied to a specified string of input bits.1 The result of the computation is the final value of all remaining bits, after all the elementary operations have been executed. It is a fundamental result in the theory of computation that just a few elementary gates suffice to evaluate any function of a finite input. This result means that with very simple hardware components, we can build up arbitrarily complex computations. So far, we have only considered a computation that acts on a particular fixed input, but we may also consider families of circuits that act on inputs of variable size. Circuit families provide a useful scheme for analyzing and classifying the complexity of computations, a scheme that will have a natural generalization when we turn to quantum computation. 6.1.2 Circuit complexity In the study of complexity, we will often be interested in functions with a one-bit output f : 0, 1 n 0, 1 . (6.20) { } →{ } 1The circuit is required to be acyclic, meaning that no directed closed loops are permitted. 6.1. CLASSICAL CIRCUITS 5 Such a function f may be said to encode a solution to a “decision problem” — the function examines the input and issues a YES or NO answer. Often, a question that would not be stated colloquially as a question with a YES/NO answer can be “repackaged” as a decision problem. For example, the function that defines the FACTORING problem is: 1 if integer x has a divisor less than y, f(x,y)= 0 otherwise; ( (6.21) knowing f(x,y) for all y<x is equivalent to knowing the least nontrivial factor of y. Another important example of a decision problem is the HAMIL- TONIAN path problem: let the input be an `-vertex graph, represented by an ` ` adjacency matrix ( a 1 in the ij entry means there is an edge linking vertices× i and j); the function is 1 if graph x has a Hamiltonian path, f(x)= (6.22) ( 0 otherwise. (A path is Hamiltonian if it visits each vertex exactly once.) We wish to gauge how hard a problem is by quantifying the resources needed to solve the problem. For a decision problem, a reasonable measure of hardness is the size of the smallest circuit that computes the corresponding function f : 0, 1 n 0, 1 . By size we mean the number of elementary gates or components{ } → that { we} must wire together to evaluate f. We may also be interested in how much time it takes to do the computation if many gates are permitted to execute in parallel. The depth of a circuit is the number of time steps required, assuming that gates acting on distinct bits can operate simultaneously (that is, the depth is the maximum length of a directed path from the input to the output of the circuit). The width of a circuit is the maximum number of gates that act in any one time step. We would like to divide the decision problems into two classes: easy and hard. But where should we draw the line? For this purpose, we consider infinite families of decision problems with variable input size; that is, where the number of bits of input can be any integer n. Then we can examine how the size of the circuit that solves the problem scales with n. If we use the scaling behavior of a circuit family to characterize the dif- ficulty of a problem, there is a subtlety. It would be cheating to hide the difficulty of the problem in the design of the circuit. Therefore, we should 6 CHAPTER 6. QUANTUM COMPUTATION restrict attention to circuit families that have acceptable “uniformity” prop- erties — it must be “easy” to build the circuit with n + 1 bits of input once we have constructed the circuit with an n-bit input. Associated with a family of functions fn (where fn has n-bit input) are circuits C that compute the functions.{ We} say that a circuit family C { n} { n} is “polynomial size” if the size of Cn grows with n no faster than a power of n, size (C ) poly (n), (6.23) n ≤ where poly denotes a polynomial. Then we define: P = decision problem solved by polynomial-size circuit families { } (P for “polynomial time”).
Load more

Recommended publications
  • An Introduction Quantum Computing
    An Introduction to Quantum Computing Michal Charemza University of Warwick March 2005 Acknowledgments Special thanks are given to Steve Flammia and Bryan Eastin, authors of the LATEX package, Qcircuit, used to draw all the quantum circuits in this document. This package is available online at http://info.phys.unm.edu/Qcircuit/. Also thanks are given to Mika Hirvensalo, author of [11], who took the time to respond to some questions. 1 Contents 1 Introduction 4 2 Quantum States 6 2.1 CompoundStates........................... 8 2.2 Observation.............................. 9 2.3 Entanglement............................. 11 2.4 RepresentingGroups . 11 3 Operations on Quantum States 13 3.1 TimeEvolution............................ 13 3.2 UnaryQuantumGates. 14 3.3 BinaryQuantumGates . 15 3.4 QuantumCircuits .......................... 17 4 Boolean Circuits 19 4.1 BooleanCircuits ........................... 19 5 Superdense Coding 24 6 Quantum Teleportation 26 7 Quantum Fourier Transform 29 7.1 DiscreteFourierTransform . 29 7.1.1 CharactersofFiniteAbelianGroups . 29 7.1.2 DiscreteFourierTransform . 30 7.2 QuantumFourierTransform. 32 m 7.2.1 Quantum Fourier Transform in F2 ............. 33 7.2.2 Quantum Fourier Transform in Zn ............. 35 8 Random Numbers 38 9 Factoring 39 9.1 OneWayFunctions.......................... 39 9.2 FactorsfromOrder.......................... 40 9.3 Shor’sAlgorithm ........................... 42 2 CONTENTS 3 10 Searching 46 10.1 BlackboxFunctions. 46 10.2 HowNotToSearch.......................... 47 10.3 Single Query Without (Grover’s) Amplification . ... 48 10.4 AmplitudeAmplification. 50 10.4.1 SingleQuery,SingleSolution . 52 10.4.2 KnownNumberofSolutions. 53 10.4.3 UnknownNumberofSolutions . 55 11 Conclusion 57 Bibliography 59 Chapter 1 Introduction The classical computer was originally a largely theoretical concept usually at- tributed to Alan Turing, called the Turing Machine.
    [Show full text]
  • Completeness of the ZX-Calculus
    Completeness of the ZX-calculus Quanlong Wang Wolfson College University of Oxford A thesis submitted for the degree of Doctor of Philosophy Hilary 2018 Acknowledgements Firstly, I would like to express my sincere gratitude to my supervisor Bob Coecke for all his huge help, encouragement, discussions and comments. I can not imagine what my life would have been like without his great assistance. Great thanks to my colleague, co-auhor and friend Kang Feng Ng, for the valuable cooperation in research and his helpful suggestions in my daily life. My sincere thanks also goes to Amar Hadzihasanovic, who has kindly shared his idea and agreed to cooperate on writing a paper based one his results. Many thanks to Simon Perdrix, from whom I have learned a lot and received much help when I worked with him in Nancy, while still benefitting from this experience in Oxford. I would also like to thank Miriam Backens for loads of useful discussions, advertising for my talk in QPL and helping me on latex problems. Special thanks to Dan Marsden for his patience and generousness in answering my questions and giving suggestions. I would like to thank Xiaoning Bian for always being ready to help me solve problems in using latex and other softwares. I also wish to thank all the people who attended the weekly ZX meeting for many interesting discussions. I am also grateful to my college advisor Jonathan Barrett and department ad- visor Jamie Vicary, thank you for chatting with me about my research and my life. I particularly want to thank my examiners, Ross Duncan and Sam Staton, for their very detailed and helpful comments and corrections by which this thesis has been significantly improved.
    [Show full text]
  • Introductory Topics in Quantum Paradigm
    Introductory Topics in Quantum Paradigm Subhamoy Maitra Indian Statistical Institute [[email protected]] 21 May, 2016 Subhamoy Maitra Quantum Information Outline of the talk Preliminaries of Quantum Paradigm What is a Qubit? Entanglement Quantum Gates No Cloning Indistinguishability of quantum states Details of BB84 Quantum Key Distribution Algorithm Description Eavesdropping State of the art: News and Industries Walsh Transform in Deutsch-Jozsa (DJ) Algorithm Deutsch-Jozsa Algorithm Walsh Transform Relating the above two Some implications Subhamoy Maitra Quantum Information Qubit and Measurement A qubit: αj0i + βj1i; 2 2 α; β 2 C, jαj + jβj = 1. Measurement in fj0i; j1ig basis: we will get j0i with probability jαj2, j1i with probability jβj2. The original state gets destroyed. Example: 1 + i 1 j0i + p j1i: 2 2 After measurement: we will get 1 j0i with probability 2 , 1 j1i with probability 2 . Subhamoy Maitra Quantum Information Basic Algebra Basic algebra: (α1j0i + β1j1i) ⊗ (α2j0i + β2j1i) = α1α2j00i + α1β2j01i + β1α2j10i + β1β2j11i, can be seen as tensor product. Any 2-qubit state may not be decomposed as above. Consider the state γ1j00i + γ2j11i with γ1 6= 0; γ2 6= 0. This cannot be written as (α1j0i + β1j1i) ⊗ (α2j0i + β2j1i). This is called entanglement. Known as Bell states or EPR pairs. An example of maximally entangled state is j00i + j11i p : 2 Subhamoy Maitra Quantum Information Quantum Gates n inputs, n outputs. Can be seen as 2n × 2n unitary matrices where the elements are complex numbers. Single input single output quantum gates. Quantum input Quantum gate Quantum Output αj0i + βj1i X βj0i + αj1i αj0i + βj1i Z αj0i − βj1i j0i+j1i j0i−|1i αj0i + βj1i H α p + β p 2 2 Subhamoy Maitra Quantum Information Quantum Gates (contd.) 1 input, 1 output.
    [Show full text]
  • A Gentle Introduction to Quantum Computing Algorithms With
    A Gentle Introduction to Quantum Computing Algorithms with Applications to Universal Prediction Elliot Catt1 Marcus Hutter1,2 1Australian National University, 2Deepmind {elliot.carpentercatt,marcus.hutter}@anu.edu.au May 8, 2020 Abstract In this technical report we give an elementary introduction to Quantum Computing for non- physicists. In this introduction we describe in detail some of the foundational Quantum Algorithms including: the Deutsch-Jozsa Algorithm, Shor’s Algorithm, Grocer Search, and Quantum Count- ing Algorithm and briefly the Harrow-Lloyd Algorithm. Additionally we give an introduction to Solomonoff Induction, a theoretically optimal method for prediction. We then attempt to use Quan- tum computing to find better algorithms for the approximation of Solomonoff Induction. This is done by using techniques from other Quantum computing algorithms to achieve a speedup in com- puting the speed prior, which is an approximation of Solomonoff’s prior, a key part of Solomonoff Induction. The major limiting factors are that the probabilities being computed are often so small that without a sufficient (often large) amount of trials, the error may be larger than the result. If a substantial speedup in the computation of an approximation of Solomonoff Induction can be achieved through quantum computing, then this can be applied to the field of intelligent agents as a key part of an approximation of the agent AIXI. arXiv:2005.03137v1 [quant-ph] 29 Apr 2020 1 Contents 1 Introduction 3 2 Preliminaries 4 2.1 Probability ......................................... 4 2.2 Computability ....................................... 5 2.3 ComplexityTheory................................... .. 7 3 Quantum Computing 8 3.1 Introduction...................................... ... 8 3.2 QuantumTuringMachines .............................. .. 9 3.2.1 Church-TuringThesis ..............................
    [Show full text]
  • BCG) Is a Global Management Consulting Firm and the World’S Leading Advisor on Business Strategy
    The Next Decade in Quantum Computing— and How to Play Boston Consulting Group (BCG) is a global management consulting firm and the world’s leading advisor on business strategy. We partner with clients from the private, public, and not-for-profit sectors in all regions to identify their highest-value opportunities, address their most critical challenges, and transform their enterprises. Our customized approach combines deep insight into the dynamics of companies and markets with close collaboration at all levels of the client organization. This ensures that our clients achieve sustainable competitive advantage, build more capable organizations, and secure lasting results. Founded in 1963, BCG is a private company with offices in more than 90 cities in 50 countries. For more information, please visit bcg.com. THE NEXT DECADE IN QUANTUM COMPUTING— AND HOW TO PLAY PHILIPP GERBERT FRANK RUESS November 2018 | Boston Consulting Group CONTENTS 3 INTRODUCTION 4 HOW QUANTUM COMPUTERS ARE DIFFERENT, AND WHY IT MATTERS 6 THE EMERGING QUANTUM COMPUTING ECOSYSTEM Tech Companies Applications and Users 10 INVESTMENTS, PUBLICATIONS, AND INTELLECTUAL PROPERTY 13 A BRIEF TOUR OF QUANTUM COMPUTING TECHNOLOGIES Criteria for Assessment Current Technologies Other Promising Technologies Odd Man Out 18 SIMPLIFYING THE QUANTUM ALGORITHM ZOO 21 HOW TO PLAY THE NEXT FIVE YEARS AND BEYOND Determining Timing and Engagement The Current State of Play 24 A POTENTIAL QUANTUM WINTER, AND THE OPPORTUNITY THEREIN 25 FOR FURTHER READING 26 NOTE TO THE READER 2 | The Next Decade in Quantum Computing—and How to Play INTRODUCTION he experts are convinced that in time they can build a Thigh-performance quantum computer.
    [Show full text]
  • Encryption of Binary and Non-Binary Data Using Chained Hadamard Transforms
    Encryption of Binary and Non-Binary Data Using Chained Hadamard Transforms Rohith Singi Reddy Computer Science Department Oklahoma State University, Stillwater, OK 74078 ABSTRACT This paper presents a new chaining technique for the use of Hadamard transforms for encryption of both binary and non-binary data. The lengths of the input and output sequence need not be identical. The method may be used also for hashing. INTRODUCTION The Hadamard transform is an example of generalized class of Fourier transforms [4], and it may be defined either recursively, or by using binary representation. Its symmetric form lends itself to applications ranging across many technical fields such as data encryption [1], signal processing [2], data compression algorithms [3], randomness measures [6], and so on. Here we consider an application of the Hadamard matrix to non-binary numbers. To do so, Hadamard transform is generalized such that all the values in the matrix are non negative. Each negative number is replaced with corresponding modulo number; for example while performing modulo 7 operations -1 is replaced with 6 to make the matrix non-binary. Only prime modulo operations are performed because non prime numbers can be divisible with numbers other than 1 and itself. The use of modulo operation along with generalized Hadamard matrix limits the output range and decreases computation complexity. In the method proposed in this paper, the input sequence is split into certain group of bits such that each group bit count is a prime number. As we are dealing with non-binary numbers, for each group of binary bits their corresponding decimal value is calculated.
    [Show full text]
  • Dominant Strategies of Quantum Games on Quantum Periodic Automata
    Computation 2015, 3, 586-599; doi:10.3390/computation3040586 OPEN ACCESS computation ISSN 2079-3197 www.mdpi.com/journal/computation Article Dominant Strategies of Quantum Games on Quantum Periodic Automata Konstantinos Giannakis y;*, Christos Papalitsas y, Kalliopi Kastampolidou y, Alexandros Singh y and Theodore Andronikos y Department of Informatics, Ionian University, 7 Tsirigoti Square, Corfu 49100, Greece; E-Mails: [email protected] (C.P.); [email protected] (K.K.); [email protected] (A.S.); [email protected] (T.A.) y These authors contributed equally to this work. * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel.: +30-266-108-7712. Academic Editor: Demos T. Tsahalis Received: 29 August 2015 / Accepted: 16 November 2015 / Published: 20 November 2015 Abstract: Game theory and its quantum extension apply in numerous fields that affect people’s social, political, and economical life. Physical limits imposed by the current technology used in computing architectures (e.g., circuit size) give rise to the need for novel mechanisms, such as quantum inspired computation. Elements from quantum computation and mechanics combined with game-theoretic aspects of computing could open new pathways towards the future technological era. This paper associates dominant strategies of repeated quantum games with quantum automata that recognize infinite periodic inputs. As a reference, we used the PQ-PENNY quantum game where the quantum strategy outplays the choice of pure or mixed strategy with probability 1 and therefore the associated quantum automaton accepts with probability 1. We also propose a novel game played on the evolution of an automaton, where players’ actions and strategies are also associated with periodic quantum automata.
    [Show full text]
  • IJSRP, Volume 2, Issue 7, July 2012 Edition
    International Journal of Scientific and Research Publications, Volume 2, Issue 7, July 2012 1 ISSN 2250-3153 OF QUANTUM GATES AND COLLAPSING STATES- A DETERMINATE MODEL APRIORI AND DIFFERENTIAL MODEL APOSTEORI 1DR K N PRASANNA KUMAR, 2PROF B S KIRANAGI AND 3 PROF C S BAGEWADI ABSTRACT : We Investigate The Holistic Model With Following Composition : ( 1) Mpc (Measurement Based Quantum Computing)(2) Preparational Methodologies Of Resource States (Application Of Electric Field Magnetic Field Etc.)For Gate Teleportation(3) Quantum Logic Gates(4) Conditionalities Of Quantum Dynamics(5) Physical Realization Of Quantum Gates(6) Selective Driving Of Optical Resonances Of Two Subsystems Undergoing Dipole-Dipole Interaction By Means Of Say Ramsey Atomic Interferometry(7) New And Efficient Quantum Algorithms For Computation(8) Quantum Entanglements And No localities(9) Computation Of Minimum Energy Of A Given System Of Particles For Experimentation(10) Exponentially Increasing Number Of Steps For Such Quantum Computation(11) Action Of The Quantum Gates(12) Matrix Representation Of Quantum Gates And Vector Constitution Of Quantum States. Stability Conditions, Analysis, Solutional Behaviour Are Discussed In Detailed For The Consummate System. The System Of Quantum Gates And Collapsing States Show Contrast With The Documented Information Thereto. Paper may be extended to exponential time with exponentially increasing steps in the computation. INTRODUCTION: LITERATURE REVIEW: Double quantum dots: interdot interactions, co-tunneling, and Kondo resonances without spin (See for details Qing-feng Sun, Hong Guo ) Authors show that through an interdot off-site electron correlation in a double quantum-dot (DQD) device, Kondo resonances emerge in the local density of states without the electron spin-degree of freedom.
    [Show full text]
  • Chapter 3 Quantum Circuit Model of Computation
    Chapter 3 Quantum Circuit Model of Computation 3.1 Introduction In the previous chapter we saw that any Boolean function can be computed from a reversible circuit. In particular, in principle at least, this can be done from a small set of universal logical gates which do not dissipate heat dissi- pation (so we have logical reversibility as well as physical reversibility. We also saw that quantum evolution of the states of a system (e.g. a collection of quantum bits) is unitary (ignoring the reading or measurement process of the bits). A unitary matrix is of course invertible, but also conserves entropy (at the present level you can think of this as a conservation of scalar prod- uct which implies conservation of probabilities and entropies). So quantum evolution is also both \logically" and physically reversible. The next natural question is then to ask if we can use quantum operations to perform computational tasks, such as computing a Boolean function? Do the principles of quantum mechanics bring in new limitations with respect to classical computations? Or do they on the contrary bring in new ressources and advantages? These issues were raised and discussed by Feynman, Benioff and Manin in the early 1980's. In principle QM does not bring any new limitations, but on the contrary the superposition principle applied to many particle systems (many qubits) enables us to perform parallel computations, thereby speed- ing up classical computations. This was recognized very early by Feynman who argued that classical computers cannot simulate efficiently quantum me- chanical processes. The basic reason is that general quantum states involve 1 2 CHAPTER 3.
    [Show full text]
  • Arxiv:2010.14788V2 [Quant-Ph] 16 Jan 2021 Detection of Ancillary Photons
    Heralded non-destructive quantum entangling gate with single-photon sources Jin-Peng Li,1, 2 Xuemei Gu,1, 2 Jian Qin,1, 2 Dian Wu,1, 2 Xiang You,1, 2 Hui Wang,1, 2 Christian Schneider,3, 4 Sven H¨ofling,2, 4 Yong-Heng Huo,1, 2 Chao-Yang Lu,1, 2 Nai-Le Liu,1, 2 Li Li,1, 2, ∗ and Jian-Wei Pan1, 2 1Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics, University of Science and Technology of China, Hefei, Anhui 230026, China 2CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics, University of Science and Technology of China, Shanghai 201315, China 3Institute of Physics, Carl von Ossietzky University, 26129 Oldenburg, Germany 4Technische Physik, Physikalische Institut and Wilhelm Conrad R¨ontgen-Centerfor Complex Material Systems, Universit¨atW¨urzburg, Am Hubland, D-97074 W¨urzburg, Germany (Dated: January 19, 2021) Heralded entangling quantum gates are an essential element for the implementation of large-scale optical quantum computation. Yet, the experimental demonstration of genuine heralded entangling gates with free-flying output photons in linear optical system, was hindered by the intrinsically probabilistic source and double-pair emission in parametric down-conversion. Here, by using an on-demand single-photon source based on a semiconductor quantum dot embedded in a micro-pillar cavity, we demonstrate a heralded controlled-NOT (CNOT) operation between two single photons for the first time. To characterize the performance of the CNOT gate, we estimate its average quantum gate fidelity of (87:8 ± 1:2)%.
    [Show full text]
  • Luigi Frunzio
    THESE DE DOCTORAT DE L’UNIVERSITE PARIS-SUD XI spécialité: Physique des solides présentée par: Luigi Frunzio pour obtenir le grade de DOCTEUR de l’UNIVERSITE PARIS-SUD XI Sujet de la thèse: Conception et fabrication de circuits supraconducteurs pour l'amplification et le traitement de signaux quantiques soutenue le 18 mai 2006, devant le jury composé de MM.: Michel Devoret Daniel Esteve, President Marc Gabay Robert Schoelkopf Rapporteurs scientifiques MM.: Antonio Barone Carlo Cosmelli ii Table of content List of Figures vii List of Symbols ix Acknowledgements xvii 1. Outline of this work 1 2. Motivation: two breakthroughs of quantum physics 5 2.1. Quantum computation is possible 5 2.1.1. Classical information 6 2.1.2. Quantum information unit: the qubit 7 2.1.3. Two new properties of qubits 8 2.1.4. Unique property of quantum information 10 2.1.5. Quantum algorithms 10 2.1.6. Quantum gates 11 2.1.7. Basic requirements for a quantum computer 12 2.1.8. Qubit decoherence 15 2.1.9. Quantum error correction codes 16 2.2. Macroscopic quantum mechanics: a quantum theory of electrical circuits 17 2.2.1. A natural test bed: superconducting electronics 18 2.2.2. Operational criteria for quantum circuits 19 iii 2.2.3. Quantum harmonic LC oscillator 19 2.2.4. Limits of circuits with lumped elements: need for transmission line resonators 22 2.2.5. Hamiltonian of a classical electrical circuit 24 2.2.6. Quantum description of an electric circuit 27 2.2.7. Caldeira-Leggett model for dissipative elements 27 2.2.8.
    [Show full text]
  • On the Significance of the Gottesman-Knill Theorem
    On the Significance of the Gottesman-Knill Theorem∗;y Michael E. Cuffaro Munich Center for Mathematical Philosophy Ludwig Maximilians Universit¨atM¨unchen 1 Introduction Toil in the field of quantum computation promises a bountiful harvest, both to the pragmatic-minded researcher seeking to develop new and efficient solutions to practi- cal problems of immediate and transparent significance, as well as to those of us moved more by philosophical concerns: we who toil in the mud and black earth, ever desirous of those remote and yet more profound insights at the root of scientific inquiry. Some of us have seen in quantum computation the promise of a solution to the interpretational debates which have characterised the foundations and philosophy of quantum mechanics since its inception. Some of us have seen the prospects for a deeper understanding of the nature of computation as such. Others have seen quantum computation as potentially illuminating our understanding of the nature and capacities of the human mind.1 An arguably more modest position (see, e.g., Aaronson, 2013; Timpson, 2013) regard- ing the philosophical interest of quantum computation (and related fields like quantum information), is that its study contributes to our understanding of the foundations of quantum mechanics and computer science mainly by offering us different perspectives on old foundational questions|fresh opportunities, that is, to reconsider just what we mean in asking these questions. One of my goals in this paper is to provide such a different perspective, on the Bell inequalities in particular. Specifically I will be arguing that the ∗Note: this is the submitted version of an article that is to appear in The British Journal for the Philosophy of Science, published by Oxford University Press.
    [Show full text]