Architecture of a Quantum Multicomputer Optimized for Shor's

Total Page:16

File Type:pdf, Size:1020Kb

Architecture of a Quantum Multicomputer Optimized for Shor's Architecture of a Quantum Multicomputer Optimized for Shor’s Factoring Algorithm A dissertation submitted to the Department of computer science of Keio University in partial fulfillment of the requirements arXiv:quant-ph/0607065 v1 11 Jul 2006 for the degree of doctor of philosophy Rodney Doyle Van Meter III July 2006 c Copyright by Rodney Doyle Van Meter III 2006 All Rights Reserved ii Abstract Quantum computers exist, and offer tantalizing possibilities of dramatic increases in computational power, but scaling them up to solve problems that are classically in- tractable offers enormous technical challenges. Distributed quantum computation of- fers a way to surpass the limitations of an individual quantum computer. I propose a quantum multicomputer as a form of distributed quantum computer. The quantum multicomputer consists of a large number of small nodes and a qubus interconnect for creating entangled state between the nodes. The primary metric chosen is the perfor- mance of such a system on Shor’s algorithm for factoring large numbers: specifically, the quantum modular exponentiation step that is the computational bottleneck. This dissertation introduces a number of optimizations for the modular exponen- tiation, including quantum versions of the classical carry-select and conditional-sum adders, improvements in the modular arithmetic, and a means for reducing the amount of expensive, error-prone quantum computation by increasing the amount of cheaper, more reliable classical computation. Parallel implementations of these circuits are eval- uated in detail for two abstract architectural models, one (called AC) which supports long-distance communication between quantum bits, or qubits, and one which allows only communication between nearest neighbors in a linear layout (called NTC). My algorithms reduce the latency, or circuit depth, to complete the modular exponentiation of an n-bit number from O(n3) to O(n log2 n) for AC and O(n2 log n) for NTC. In- cluding improvements in the constant factors, calculations show that these algorithms are one million times and thirteen thousand times faster on AC and NTC, respectively, when factoring a 6,000-bit number. These circuits also reduce the demands on quantum error correction from 210n4 to 12n3 log n for AC and 3n4 for NTC, potentially ∼ ∼ 2 ∼ reducing the number of levels of error-correction encoding or allowing execution on more error-prone hardware. Extending to the quantum multicomputer, I calculate the performance of several types of adder circuits for several different hardware configurations. Five different iii qubus interconnect topologies and two different node sizes are considered, and two forms of carry-ripple adder are found to be the fastest for a wide range of performance parameters. Small nodes (up to five logical qubits) and a linear interconnection network provide adequate performance; more complex networks are unnecessary until n reaches several hundred bits. As node size grows, it is important that the I/O bandwidth of a node grow, as well, or performance can actually decline despite the overall decrease in network activity. The links in the quantum multicomputer are serial; parallel links would provide only very modest improvements in system reliability and performance. Two levels of the Steane [[23,1,7]] error correction code will adequately protect our data for factoring a 1,024-bit number even when the qubit teleportation failure rate is one percent. iv Acknowledgements I had the good fortune to become acquainted very early with some characters of very high standing, and to feel the incessant wish that I could even become what they were. Thomas Jefferson, Autobiography Many, many people have demonstrated a faith in me that can never be repaid, start- ing with my parents, who never even suggested, so far as I recall, that there were any limits to what I could accomplish (despite sometimes overwhelming evidence to the contrary). (Though, at the same time, I have no recollection that they ever suggested I had a future in the NBA.) Most especially, I must thank my daughters Sophia and Esther and my wife Mayumi, who put up with many hours of Daddy being physically present but mentally elsewhere. My sisters Sheila and Lera for nearly forty years have suffered the indignities and lo- gistical difficulties of a weird, sartorially challenged older brother who lives thousands of miles away. To my grandparents, aunts, uncles, cousins and brother-in-law in a large and close family I also owe an apology for living so far away. Every life has its cusps, its turning points that forever change you. The biggest was Caltech, but joining ISI was an unanticipated stroke of fortune. The friends and men- tors I made in the Caltech and ISI days still carry me forward. Ross Berteig dragged me to the three most life-changing classes I took at Caltech (Feynman, Ayres, and Scudder), including the one that led me to ISI. At ISI, I met Wook, and my life would never be the same in ways beyond enumerating; I owe no one a greater debt. Dale Chase taught me how to be a good employee and person (and how to play team vol- leyball). Andi, Bobo, Brenda, Daryll, Dave, Dennis, Edie, Gabrielle, Grace, Greg, Harold, Hugo, Irene, Jessica, John, John, Kevin, Kyu, Liralen, Mimi, Michelle, Min, Myles, Pam, Rick, Ryuji, Sandy, Steve, Suz, Tiger, Yosufi and the entire CINC-PAC, WoW, volleyball, Half Moon Bay, Quantum, Nokia, NII, Keio, and Network Alchemy v crowds, just for being there (wherever “there” happens to be). Thanks to Jim Hughes for help on classical cryptography, Reagan Moore for su- percomputing advice, and the rest of the MSSTC EC for years of companionship and learning. Without the support and encouragement of Takashi and Nobunori Shigezaki, Mark Holzbach and the folks at Asaca and ShibaSoku, and the teaching of Yuko Yamaguchi at Kichijoji Language School and Misaki-sensei at Keio, Japan would have remained a remote, foreign land rather than the second home it has become. When I began working on quantum computing three years ago, I received important early encouragement from Prof. Kohei Itoh and Eisuke Abe of Keio University, Prof. Kunihiro of the University of Electro-Communications, Drs. Kawano and Takahashi of NTT CRL, Prof. Iwama of Kyoto University, Prof. Yamashita of NAIST, Dave Bacon of Washington, Mark Oskin of Washington, and Dr. Yamaguchi of Stanford. Professors Yoshi Yamamoto, Seth Lloyd, Isaac Chuang, Andrew Steane, Mio Murao, Hiroshi Imai, Seigo Tarucha and Akira Furusawa, and Yasunobu Nakamura and J.W. Tsai of NEC, and many researchers at NTT provided access to their labs and students, without which I would never understand how to actually build a quantum computer. Prof. Yamamoto and the others who created and staffed the summer schools in Okinawa and Kochi not only taught me but brought me into their community. I look forward to deepening collaborations with all of you over the coming years. Although my name goes on the thesis, my coauthors on the half-dozen papers that are incorporated deserve much of the credit: Kohei Itoh, Mark Oskin, Thaddeus Ladd, Kae Nemoto, and Bill Munro. Thaddeus gets a special call out for writing advice as well as teaching me physics. Joe Touch, Ted Faber, Bill Manning and Nick Burke all read (sometimes awful) drafts of various papers and provided other important support. Kevin Binkley’s stochastic engine, especially the genetic algorithms, provided insight into optimization problems that remain open. Suzanne and Bob Diller get credit for cogent advice on the title and abstract of my dissertation, as well as providing years of friendship. Michael Cohen and Prof. Sagawa of Aizu-Wakamatsu and Prof. Jun Murai, Ryuji Wakikawa and Shoko Mikawa at the School of Internet ASIA Project provided teaching opportunities which turned into wonderful learning experiences for me. Chip Elliott and others at BBN have provided important encouragement, as well. The music of Tatopani, Billy Higgins, John Coltrane, Louis Armstrong, King Crim- son, Kodo, and a plethora of others kept me sane. Surprisingly, we have no favorite chef vi in this country (our favorite anywhere is Jose Luis Ugalde of Cafe Gibraltar), but ev- eryone who has fed me startling and wonderful meals – you keep me going. Without the cooking and baby-sitting of the indomitable Kazuko Arai, this thesis would have taken a decade to complete, if it ever got done at all. This work was supported in part by Ken Adelman and Dave Kashtan under the Network Alchemy basic research funding plan, and by CREST-JST. Kae Nemoto also provided funding for travel and a desk at NII. Karl and Pattie and Danner and Jenny put me up in Cambridge. This thesis was created using 100% free software. Thanks to the creators of Linux, X, TEXand LATEX, GNU Emacs, xfig, dia, POVray, maxima/MACSYMA, octave, gnu- plot, bison, flex, gcc, and more — and, in some cases, to the researchers on whose work these tools are founded, though the code base has changed. And thanks to thecreators of the arXiv, scholar.google.com, researchindex.org, and citebase, without which I would have missed much important research, and probably been forced to recreate it poorly and tediously on my own. I thank Y. Nakamura, T. Yamamoto, D. Wineland, and K. M. Itoh for the figures. Thanks to all of the patient physicists who have put up with my slowness, including Viv Kendon for help with Shor’s algorithm. I have had, in effect, four advisers: Fumio Teraoka in computer networking, Kohei Itoh in experimental physics, and Kae Nemoto and Bill Munro in theoretical physics. All four have been oustanding. Kae worked harder on reviewing this dissertation than anyone else; it would not be as clear and correct without her. Bill is the one person not on my committee without whom this technical work could not have been done.
Recommended publications
  • Efficient Regular Modular Exponentiation Using
    J Cryptogr Eng (2017) 7:245–253 DOI 10.1007/s13389-016-0134-5 SHORT COMMUNICATION Efficient regular modular exponentiation using multiplicative half-size splitting Christophe Negre1,2 · Thomas Plantard3,4 Received: 14 August 2015 / Accepted: 23 June 2016 / Published online: 13 July 2016 © Springer-Verlag Berlin Heidelberg 2016 Abstract In this paper, we consider efficient RSA modular x K mod N where N = pq with p and q prime. The private exponentiations x K mod N which are regular and con- data are the two prime factors of N and the private exponent stant time. We first review the multiplicative splitting of an K used to decrypt or sign a message. In order to insure a integer x modulo N into two half-size integers. We then sufficient security level, N and K are chosen large enough take advantage of this splitting to modify the square-and- to render the factorization of N infeasible: they are typically multiply exponentiation as a regular sequence of squarings 2048-bit integers. The basic approach to efficiently perform always followed by a multiplication by a half-size inte- the modular exponentiation is the square-and-multiply algo- ger. The proposed method requires around 16% less word rithm which scans the bits ki of the exponent K and perform operations compared to Montgomery-ladder, square-always a sequence of squarings followed by a multiplication when and square-and-multiply-always exponentiations. These the- ki is equal to one. oretical results are validated by our implementation results When the cryptographic computations are performed on which show an improvement by more than 12% compared an embedded device, an adversary can monitor power con- approaches which are both regular and constant time.
    [Show full text]
  • Miller-Rabin Primality Test (Java)
    Miller-Rabin primality test (Java) Other implementations: C | C, GMP | Clojure | Groovy | Java | Python | Ruby | Scala The Miller-Rabin primality test is a simple probabilistic algorithm for determining whether a number is prime or composite that is easy to implement. It proves compositeness of a number using the following formulas: Suppose 0 < a < n is coprime to n (this is easy to test using the GCD). Write the number n−1 as , where d is odd. Then, provided that all of the following formulas hold, n is composite: for all If a is chosen uniformly at random and n is prime, these formulas hold with probability 1/4. Thus, repeating the test for k random choices of a gives a probability of 1 − 1 / 4k that the number is prime. Moreover, Gerhard Jaeschke showed that any 32-bit number can be deterministically tested for primality by trying only a=2, 7, and 61. [edit] 32-bit integers We begin with a simple implementation for 32-bit integers, which is easier to implement for reasons that will become apparent. First, we'll need a way to perform efficient modular exponentiation on an arbitrary 32-bit integer. We accomplish this using exponentiation by squaring: Source URL: http://www.en.literateprograms.org/Miller-Rabin_primality_test_%28Java%29 Saylor URL: http://www.saylor.org/courses/cs409 ©Spoon! (http://www.en.literateprograms.org/Miller-Rabin_primality_test_%28Java%29) Saylor.org Used by Permission Page 1 of 5 <<32-bit modular exponentiation function>>= private static int modular_exponent_32(int base, int power, int modulus) { long result = 1; for (int i = 31; i >= 0; i--) { result = (result*result) % modulus; if ((power & (1 << i)) != 0) { result = (result*base) % modulus; } } return (int)result; // Will not truncate since modulus is an int } int is a 32-bit integer type and long is a 64-bit integer type.
    [Show full text]
  • RSA Power Analysis Obfuscation: a Dynamic FPGA Architecture John W
    Air Force Institute of Technology AFIT Scholar Theses and Dissertations Student Graduate Works 3-22-2012 RSA Power Analysis Obfuscation: A Dynamic FPGA Architecture John W. Barron Follow this and additional works at: https://scholar.afit.edu/etd Part of the Electrical and Computer Engineering Commons Recommended Citation Barron, John W., "RSA Power Analysis Obfuscation: A Dynamic FPGA Architecture" (2012). Theses and Dissertations. 1078. https://scholar.afit.edu/etd/1078 This Thesis is brought to you for free and open access by the Student Graduate Works at AFIT Scholar. It has been accepted for inclusion in Theses and Dissertations by an authorized administrator of AFIT Scholar. For more information, please contact [email protected]. RSA POWER ANALYSIS OBFUSCATION: A DYNAMIC FPGA ARCHITECTURE THESIS John W. Barron, Captain, USAF AFIT/GE/ENG/12-02 DEPARTMENT OF THE AIR FORCE AIR UNIVERSITY AIR FORCE INSTITUTE OF TECHNOLOGY Wright-Patterson Air Force Base, Ohio APPROVED FOR PUBLIC RELEASE; DISTRIBUTION UNLIMITED. The views expressed in this thesis are those of the author and do not reflect the official policy or position of the United States Air Force, Department of Defense, or the United States Government. This material is declared a work of the U.S. Government and is not subject to copyright protection in the United States. AFIT/GE/ENG/12-02 RSA POWER ANALYSIS OBFUSCATION: A DYNAMIC FPGA ARCHITECTURE THESIS Presented to the Faculty Department of Electrical and Computer Engineering Graduate School of Engineering and Management Air Force Institute of Technology Air University Air Education and Training Command In Partial Fulfillment of the Requirements for the Degree of Master of Science in Electrical Engineering John W.
    [Show full text]
  • THE MILLER–RABIN PRIMALITY TEST 1. Fast Modular
    THE MILLER{RABIN PRIMALITY TEST 1. Fast Modular Exponentiation Given positive integers a, e, and n, the following algorithm quickly computes the reduced power ae % n. • (Initialize) Set (x; y; f) = (1; a; e). • (Loop) While f > 1, do as follows: { If f%2 = 0 then replace (x; y; f) by (x; y2 % n; f=2), { otherwise replace (x; y; f) by (xy % n; y; f − 1). • (Terminate) Return x. The algorithm is strikingly efficient both in speed and in space. To see that it works, represent the exponent e in binary, say e = 2f + 2g + 2h; 0 ≤ f < g < h: The algorithm successively computes (1; a; 2f + 2g + 2h) f (1; a2 ; 1 + 2g−f + 2h−f ) f f (a2 ; a2 ; 2g−f + 2h−f ) f g (a2 ; a2 ; 1 + 2h−g) f g g (a2 +2 ; a2 ; 2h−g) f g h (a2 +2 ; a2 ; 1) f g h h (a2 +2 +2 ; a2 ; 0); and then it returns the first entry, which is indeed ae. 2. The Fermat Test and Fermat Pseudoprimes Fermat's Little Theorem states that for any positive integer n, if n is prime then bn mod n = b for b = 1; : : : ; n − 1. In the other direction, all we can say is that if bn mod n = b for b = 1; : : : ; n − 1 then n might be prime. If bn mod n = b where b 2 f1; : : : ; n − 1g then n is called a Fermat pseudoprime base b. There are 669 primes under 5000, but only five values of n (561, 1105, 1729, 2465, and 2821) that are Fermat pseudoprimes base b for b = 2; 3; 5 without being prime.
    [Show full text]
  • Modular Exponentiation: Exercises
    Modular Exponentiation: Exercises 1. Compute the following using the method of successive squaring: (a) 250 (mod 101) (b) 350 (mod 101) (c) 550 (mod 101). 2. Using an example from this lecture, compute 450 (mod 101) with no effort. How did you do it? 3. Explain how we could have predicted the answer to problem 1(a) with no effort. 4. Compute the following using the method of successive squaring: 50 58 44 (a) (3) in Z=101Z (b) (3) in Z=61Z (c)(4) in Z=51Z. 5000 5. Compute (78) in Z=79Z, and explain why this calculation is so very trivial. 4999 What is (78) in Z=79Z? 60 6. Fermat's Little Theorem says that (3) = 1 in Z=61Z. Use this fact to 58 compute (3) in Z=61Z (see problem 4(b) above) without using successive squaring, but by computing the inverse of (3)2 instead, for instance by the Euclidean algorithm. Explain why this works. 7. We may see later on that the set of all a 2 Z=mZ such that gcd(a; m) = 1 is a group. Let '(m) be the number of elements in this group, which is often × × denoted by (Z=mZ) . It turns out that for each a 2 (Z=mZ) , some power × of a must be equal to 1. The order of any a in the group (Z=mZ) is by definition the smallest positive integer e such that (a)e = 1. × (a) Compute the orders of all the elements of (Z=11Z) . × (b) Compute the orders of all the elements of (Z=17Z) .
    [Show full text]
  • Lecture 19 1 Readings 2 Introduction 3 Shor's Order-Finding Algorithm
    C/CS/Phys 191 Shor’s order (period) finding algorithm and factoring 11/01/05 Fall 2005 Lecture 19 1 Readings Benenti et al., Ch. 3.12 - 3.14 Stolze and Suter, Quantum Computing, Ch. 8.3 Nielsen and Chuang, Quantum Computation and Quantum Information, Ch. 5.2 - 5.3, 5.4.1 (NC use phase estimation for this, which we present in the next lecture) literature: Ekert and Jozsa, Rev. Mod. Phys. 68, 733 (1996) 2 Introduction With a fast algorithm for the Quantum Fourier Transform in hand, it is clear that many useful applications should be possible. Fourier transforms are typically used to extract the periodic components in functions, so this is an immediate one. One very important example is finding the period of a modular exponential function, which is also known as order-finding. This is a key element of Shor’s algorithm to factor large integers N. In Shor’s algorithm, the quantum algorithm for order-finding is combined with a series of efficient classical computational steps to make an algorithm that is overall polynomial in the input size 2 n = log2N, scaling as O(n lognloglogn). This is better than the best known classical algorithm, the number field sieve, which scales superpolynomially in n, i.e., as exp(O(n1/3(logn)2/3)). In this lecture we shall first present the quantum algorithm for order-finding and then summarize how this is used together with tools from number theory to efficiently factor large numbers. 3 Shor’s order-finding algorithm 3.1 modular exponentiation Recall the exponential function ax.
    [Show full text]
  • Number Theory
    CS 5002: Discrete Structures Fall 2018 Lecture 5: October 4, 2018 1 Instructors: Tamara Bonaci, Adrienne Slaugther Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. Number Theory Readings for this week: Rosen, Chapter 4.1, 4.2, 4.3, 4.4 5.1 Overview 1. Review: set theory 2. Review: matrices and arrays 3. Number theory: divisibility and modular arithmetic 4. Number theory: prime numbers and greatest common divisor (gcd) 5. Number theory: solving congruences 6. Number theory: modular exponentiation and Fermat's little theorem 5.2 Introduction In today's lecture, we will dive into the branch of mathematics, studying the set of integers and their properties, known as number theory. Number theory has very important practical implications in computer science, but also in our every day life. For example, secure online communication, as we know it today, would not be possible without number theory because many of the encryption algorithms used to enable secure communication rely heavily of some famous (and in some cases, very old) results from number theory. We will first introduce the notion of divisibility of integers. From there, we will introduce modular arithmetic, and explore and prove some important results about modular arithmetic. We will then discuss prime numbers, and show that there are infinitely many primes. Finaly, we will explain how to solve linear congruences, and systems of linear congruences. 5-1 5-2 Lecture 5: October 4, 2018 5.3 Review 5.3.1 Set Theory In the last lecture, we talked about sets, and some of their properties.
    [Show full text]
  • Lecture Slides
    CSE 291-I: Applied Cryptography Nadia Heninger UCSD Spring 2020 Lecture 8 Legal Notice The Zoom session for this class will be recorded and made available asynchronously on Canvas to registered students. Announcements 1. HW 3 is due today! Volunteer to grade! 2. HW 4 is due before class in 1 week, April 29. I fixed an error so check the web page again: API_KEY should be 256 bits. Last time: Authenticated encryption This time: Number theory review Fundamental theorem of arithemtic Theorem e e Every n Z n = 0 has unique factorization n = p 1 p 2 ...per 2 6 ± 1 2 r with pi distinct primes and ei positive integers. Division and remainder Theorem a, b Z, b > 0, unique q, r Z s.t. a = bq + r, 0 r < b. 2 9 2 r a mod bamod b = a b a ⌘ − b b c Because we’re in CS, we also write r = a mod b. b a a mod b = 0 | () a = b mod N: (a mod N)=(b mod N) a = b mod N N (a b) () | − - l isan ok N l (b-a) too l ) multiplier Proof. Let I = sa + rb r, s Z Let d be the smallest positive elt. of I . { | 2 } d divides every element of I : • 1. Choose c = sc a + rc b. 2. c = qd + r: r = c qd = s a r b q(ax +by )=(s qx)a+(r qy)b I − c − c − c − c − 2 Thus r = 0byminimalityofd, thus d c. | d is largest: Assume d > d s.t.
    [Show full text]
  • Phatak Primality Test (PPT)
    PPT : New Low Complexity Deterministic Primality Tests Leveraging Explicit and Implicit Non-Residues A Set of Three Companion Manuscripts PART/Article 1 : Introducing Three Main New Primality Conjectures: Phatak’s Baseline Primality (PBP) Conjecture , and its extensions to Phatak’s Generalized Primality Conjecture (PGPC) , and Furthermost Generalized Primality Conjecture (FGPC) , and New Fast Primailty Testing Algorithms Based on the Conjectures and other results. PART/Article 2 : Substantial Experimental Data and Evidence1 PART/Article 3 : Analytic Proofs of Baseline Primality Conjecture for Special Cases Dhananjay Phatak ([email protected]) and Alan T. Sherman2 and Steven D. Houston and Andrew Henry (CSEE Dept. UMBC, 1000 Hilltop Circle, Baltimore, MD 21250, U.S.A.) 1 No counter example has been found 2 Phatak and Sherman are affiliated with the UMBC Cyber Defense Laboratory (CDL) directed by Prof. Alan T. Sherman Overall Document (set of 3 articles) – page 1 First identification of the Baseline Primality Conjecture @ ≈ 15th March 2018 First identification of the Generalized Primality Conjecture @ ≈ 10th June 2019 Last document revision date (time-stamp) = August 19, 2019 Color convention used in (the PDF version) of this document : All clickable hyper-links to external web-sites are brown : For example : G. E. Pinch’s excellent data-site that lists of all Carmichael numbers <10(18) . clickable hyper-links to references cited appear in magenta. Ex : G.E. Pinch’s web-site mentioned above is also accessible via reference [1] All other jumps within the document appear in darkish-green color. These include Links to : Equations by the number : For example, the expression for BCC is specified in Equation (11); Links to Tables, Figures, and Sections or other arbitrary hyper-targets.
    [Show full text]
  • LARGE PRIME NUMBERS This Writeup Is Modeled Closely on A
    LARGE PRIME NUMBERS This writeup is modeled closely on a writeup by Paul Garrett. See, for example, http://www-users.math.umn.edu/~garrett/crypto/overview.pdf 1. Fast Modular Exponentiation Given positive integers a, e, and n, the following algorithm quickly computes the reduced power ae % n. (Here x % n denotes the element of f0; ··· ; n − 1g that is congruent to x modulo n. Note that x % n is not an element of Z=nZ since such elements are cosets rather than coset representatives.) • (Initialize) Set (x; y; f) = (1; a; e). • (Loop) While f > 0, do as follows: { If f%2 = 0 then replace (x; y; f) by (x; y2 % n; f=2), { otherwise replace (x; y; f) by (xy % n; y; f − 1). • (Terminate) Return x. The algorithm is strikingly efficient both in speed and in space. Especially, the operations on f (halving it when it is even, decrementing it when it is odd) are very simple in binary. To see that the algorithm works, represent the exponent e in binary, say e = 2g + 2h + 2k; 0 ≤ g < h < k: The algorithm successively computes (1; a; 2g + 2h + 2k) g (1; a2 ; 1 + 2h−g + 2k−g) g g (a2 ; a2 ; 2h−g + 2k−g) g h (a2 ; a2 ; 1 + 2k−h) g h h (a2 +2 ; a2 ; 2k−h) g h k (a2 +2 ; a2 ; 1) g h k k (a2 +2 +2 ; a2 ; 0); and then it returns the first entry, which is indeed ae. Fast modular exponentiation is not only for computers. For example, to compute 237 % 149, proceed as follows, (1; 2; 37) ! (2; 2; 36) ! (2; 4; 18) ! (2; 16; 9) ! (32; 16; 8) ! (32; −42; 4) ! (32; −24; 2) ! (32; −20; 1) ! ( 105 ; −20; 0): 2.
    [Show full text]
  • With Animation
    Integer Arithmetic Arithmetic in Finite Fields Arithmetic of Elliptic Curves Public-key Cryptography Theory and Practice Abhijit Das Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Chapter 3: Algebraic and Number-theoretic Computations Public-key Cryptography: Theory and Practice Abhijit Das Integer Arithmetic GCD Arithmetic in Finite Fields Modular Exponentiation Arithmetic of Elliptic Curves Primality Testing Integer Arithmetic Public-key Cryptography: Theory and Practice Abhijit Das Integer Arithmetic GCD Arithmetic in Finite Fields Modular Exponentiation Arithmetic of Elliptic Curves Primality Testing Integer Arithmetic In cryptography, we deal with very large integers with full precision. Public-key Cryptography: Theory and Practice Abhijit Das Integer Arithmetic GCD Arithmetic in Finite Fields Modular Exponentiation Arithmetic of Elliptic Curves Primality Testing Integer Arithmetic In cryptography, we deal with very large integers with full precision. Standard data types in programming languages cannot handle big integers. Public-key Cryptography: Theory and Practice Abhijit Das Integer Arithmetic GCD Arithmetic in Finite Fields Modular Exponentiation Arithmetic of Elliptic Curves Primality Testing Integer Arithmetic In cryptography, we deal with very large integers with full precision. Standard data types in programming languages cannot handle big integers. Special data types (like arrays of integers) are needed. Public-key Cryptography: Theory and Practice Abhijit Das Integer Arithmetic GCD Arithmetic in Finite Fields Modular Exponentiation Arithmetic of Elliptic Curves Primality Testing Integer Arithmetic In cryptography, we deal with very large integers with full precision. Standard data types in programming languages cannot handle big integers. Special data types (like arrays of integers) are needed. The arithmetic routines on these specific data types have to be implemented.
    [Show full text]
  • Primality Testing
    Syracuse University SURFACE Electrical Engineering and Computer Science - Technical Reports College of Engineering and Computer Science 6-1992 Primality Testing Per Brinch Hansen Syracuse University, School of Computer and Information Science, [email protected] Follow this and additional works at: https://surface.syr.edu/eecs_techreports Part of the Computer Sciences Commons Recommended Citation Hansen, Per Brinch, "Primality Testing" (1992). Electrical Engineering and Computer Science - Technical Reports. 169. https://surface.syr.edu/eecs_techreports/169 This Report is brought to you for free and open access by the College of Engineering and Computer Science at SURFACE. It has been accepted for inclusion in Electrical Engineering and Computer Science - Technical Reports by an authorized administrator of SURFACE. For more information, please contact [email protected]. SU-CIS-92-13 Primality Testing Per Brinch Hansen June 1992 School of Computer and Information Science Syracuse University Suite 4-116, Center for Science and Technology Syracuse, NY 13244-4100 Primality Testing1 PER BRINCH HANSEN Syracuse University, Syracuse, New York 13244 June 1992 This tutorial describes the Miller-Rabin method for testing the primality of large integers. The method is illustrated by a Pascal algorithm. The performance of the algorithm was measured on a Computing Surface. Categories and Subject Descriptors: G.3 [Probability and Statistics: [Proba­ bilistic algorithms (Monte Carlo) General Terms: Algorithms Additional Key Words and Phrases: Primality testing CONTENTS INTRODUCTION 1. FERMAT'S THEOREM 2. THE FERMAT TEST 3. QUADRATIC REMAINDERS 4. THE MILLER-RABIN TEST 5. A PROBABILISTIC ALGORITHM 6. COMPLEXITY 7. EXPERIMENTS 8. SUMMARY ACKNOWLEDGEMENTS REFERENCES 1Copyright@1992 Per Brinch Hansen Per Brinch Hansen: Primality Testing 2 INTRODUCTION This tutorial describes a probabilistic method for testing the primality of large inte­ gers.
    [Show full text]