Quantum Vs. Classical Proofs and Advice
Total Page:16
File Type:pdf, Size:1020Kb
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]. -
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}. -
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. -
Quantum Machine Learning: Benefits and Practical Examples
Quantum Machine Learning: Benefits and Practical Examples Frank Phillipson1[0000-0003-4580-7521] 1 TNO, Anna van Buerenplein 1, 2595 DA Den Haag, The Netherlands [email protected] Abstract. A quantum computer that is useful in practice, is expected to be devel- oped in the next few years. An important application is expected to be machine learning, where benefits are expected on run time, capacity and learning effi- ciency. In this paper, these benefits are presented and for each benefit an example application is presented. A quantum hybrid Helmholtz machine use quantum sampling to improve run time, a quantum Hopfield neural network shows an im- proved capacity and a variational quantum circuit based neural network is ex- pected to deliver a higher learning efficiency. Keywords: Quantum Machine Learning, Quantum Computing, Near Future Quantum Applications. 1 Introduction Quantum computers make use of quantum-mechanical phenomena, such as superposi- tion and entanglement, to perform operations on data [1]. Where classical computers require the data to be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits, which can be in superpositions of states. These computers would theoretically be able to solve certain problems much more quickly than any classical computer that use even the best cur- rently known algorithms. Examples are integer factorization using Shor's algorithm or the simulation of quantum many-body systems. This benefit is also called ‘quantum supremacy’ [2], which only recently has been claimed for the first time [3]. There are two different quantum computing paradigms. -
Chapter 1 Chapter 2 Preliminaries Measurements
To Vera Rae 3 Contents 1 Preliminaries 14 1.1 Elements of Linear Algebra ............................ 17 1.2 Hilbert Spaces and Dirac Notations ........................ 23 1.3 Hermitian and Unitary Operators; Projectors. .................. 27 1.4 Postulates of Quantum Mechanics ......................... 34 1.5 Quantum State Postulate ............................. 36 1.6 Dynamics Postulate ................................. 42 1.7 Measurement Postulate ............................... 47 1.8 Linear Algebra and Systems Dynamics ...................... 50 1.9 Symmetry and Dynamic Evolution ........................ 52 1.10 Uncertainty Principle; Minimum Uncertainty States ............... 54 1.11 Pure and Mixed Quantum States ......................... 55 1.12 Entanglement; Bell States ............................. 57 1.13 Quantum Information ............................... 59 1.14 Physical Realization of Quantum Information Processing Systems ....... 65 1.15 Universal Computers; The Circuit Model of Computation ............ 68 1.16 Quantum Gates, Circuits, and Quantum Computers ............... 74 1.17 Universality of Quantum Gates; Solovay-Kitaev Theorem ............ 79 1.18 Quantum Computational Models and Quantum Algorithms . ........ 82 1.19 Deutsch, Deutsch-Jozsa, Bernstein-Vazirani, and Simon Oracles ........ 89 1.20 Quantum Phase Estimation ............................ 96 1.21 Walsh-Hadamard and Quantum Fourier Transforms ...............102 1.22 Quantum Parallelism and Reversible Computing .................107 1.23 Grover Search Algorithm -
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. -
Quantum Algorithms
An Introduction to Quantum Algorithms Emma Strubell COS498 { Chawathe Spring 2011 An Introduction to Quantum Algorithms Contents Contents 1 What are quantum algorithms? 3 1.1 Background . .3 1.2 Caveats . .4 2 Mathematical representation 5 2.1 Fundamental differences . .5 2.2 Hilbert spaces and Dirac notation . .6 2.3 The qubit . .9 2.4 Quantum registers . 11 2.5 Quantum logic gates . 12 2.6 Computational complexity . 19 3 Grover's Algorithm 20 3.1 Quantum search . 20 3.2 Grover's algorithm: How it works . 22 3.3 Grover's algorithm: Worked example . 24 4 Simon's Algorithm 28 4.1 Black-box period finding . 28 4.2 Simon's algorithm: How it works . 29 4.3 Simon's Algorithm: Worked example . 32 5 Conclusion 33 References 34 Page 2 of 35 An Introduction to Quantum Algorithms 1. What are quantum algorithms? 1 What are quantum algorithms? 1.1 Background The idea of a quantum computer was first proposed in 1981 by Nobel laureate Richard Feynman, who pointed out that accurately and efficiently simulating quantum mechanical systems would be impossible on a classical computer, but that a new kind of machine, a computer itself \built of quantum mechanical elements which obey quantum mechanical laws" [1], might one day perform efficient simulations of quantum systems. Classical computers are inherently unable to simulate such a system using sub-exponential time and space complexity due to the exponential growth of the amount of data required to completely represent a quantum system. Quantum computers, on the other hand, exploit the unique, non-classical properties of the quantum systems from which they are built, allowing them to process exponentially large quantities of information in only polynomial time. -
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). -
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). -
Quantum Computing : a Gentle Introduction / Eleanor Rieffel and Wolfgang Polak
QUANTUM COMPUTING A Gentle Introduction Eleanor Rieffel and Wolfgang Polak The MIT Press Cambridge, Massachusetts London, England ©2011 Massachusetts Institute of Technology All rights reserved. No part of this book may be reproduced in any form by any electronic or mechanical means (including photocopying, recording, or information storage and retrieval) without permission in writing from the publisher. For information about special quantity discounts, please email [email protected] This book was set in Syntax and Times Roman by Westchester Book Group. Printed and bound in the United States of America. Library of Congress Cataloging-in-Publication Data Rieffel, Eleanor, 1965– Quantum computing : a gentle introduction / Eleanor Rieffel and Wolfgang Polak. p. cm.—(Scientific and engineering computation) Includes bibliographical references and index. ISBN 978-0-262-01506-6 (hardcover : alk. paper) 1. Quantum computers. 2. Quantum theory. I. Polak, Wolfgang, 1950– II. Title. QA76.889.R54 2011 004.1—dc22 2010022682 10987654321 Contents Preface xi 1 Introduction 1 I QUANTUM BUILDING BLOCKS 7 2 Single-Qubit Quantum Systems 9 2.1 The Quantum Mechanics of Photon Polarization 9 2.1.1 A Simple Experiment 10 2.1.2 A Quantum Explanation 11 2.2 Single Quantum Bits 13 2.3 Single-Qubit Measurement 16 2.4 A Quantum Key Distribution Protocol 18 2.5 The State Space of a Single-Qubit System 21 2.5.1 Relative Phases versus Global Phases 21 2.5.2 Geometric Views of the State Space of a Single Qubit 23 2.5.3 Comments on General Quantum State Spaces -
Quantum Algorithms for Linear Algebra and Machine Learning
Quantum Algorithms for Linear Algebra and Machine Learning. Anupam Prakash Electrical Engineering and Computer Sciences University of California at Berkeley Technical Report No. UCB/EECS-2014-211 http://www.eecs.berkeley.edu/Pubs/TechRpts/2014/EECS-2014-211.html December 9, 2014 Copyright © 2014, by the author(s). All rights reserved. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission. Quantum Algorithms for Linear Algebra and Machine Learning by Anupam Prakash B.Tech., IIT Kharagpur 2008 A dissertation submitted in partial satisfaction of the requirements for the degree of Doctor of Philosophy in Electrical Engineering and Computer Sciences in the GRADUATE DIVISION of the UNIVERSITY OF CALIFORNIA, BERKELEY Committee in charge: Professor Umesh Vazirani, Chair Professor Satish Rao Professor Ashvin Vishwanath Fall 2014 Quantum Algorithms for Linear Algebra and Machine Learning Copyright c 2014 by Anupam Prakash Abstract Quantum Algorithms for Linear Algebra and Machine Learning by Anupam Prakash Doctor of Philosophy in Electrical Engineering and Computer Sciences University of California, Berkeley Professor Umesh Vazirani, Chair Most quantum algorithms offering speedups over classical algorithms are based on the three tech- niques of phase estimation, amplitude estimation and Hamiltonian simulation. In spite of the linear algebraic nature of the postulates of quantum mechanics, until recent work by Lloyd and coauthors (23; 22; 24) no quantum algorithms achieving speedups for linear algebra or machine learning had been proposed. -
ADVANCED QUANTUM INFORMATION THEORY Contents
CDT in Quantum Engineering Spring 2016 ADVANCED QUANTUM INFORMATION THEORY Lecture notes Ashley Montanaro, University of Bristol [email protected] Contents 1 Introduction 2 2 Classical and quantum computational complexity4 3 Grover's algorithm 10 Health warning: There are likely to be changes and corrections to these notes throughout the unit. For updates, see http://www.maths.bris.ac.uk/~csxam/aqit2016/. Version 1.0 (January 11, 2016). 1 1 Introduction The field of quantum information theory studies the remarkable ways in which quantum information { and the processing thereof { differs from information stored in classical systems. Nowhere is this difference more pronounced than the dramatic speedups obtained by quantum computation over classical computation. These notes aim to cover (some of) the theoretical topics which any self-respecting quantum information theorist, or experimentalist working in the field of quantum information processing, should know. These include the famous algorithms of Shor and Grover, and the simulation of quantum systems; the more recent topic of quantum walk algorithms; decoherence and quantum error-correction. 1.1 Complementary reading These lecture notes have benefited significantly from the expositions in the following lecture courses, which may be of interest for background reading: • Quantum Computation, Richard Jozsa, University of Cambridge http://www.qi.damtp.cam.ac.uk/node/261 The material here on the QFT and Shor's algorithm follows this exposition closely. • Quantum Algorithms, Andrew Childs, University of Maryland http://cs.umd.edu/~amchilds/qa/ A combination of lecture notes for three graduate courses. The material here on quantum walk algorithms is based on the exposition in these notes.