DRAFT NISTIR 8105, Report on Post-Quantum Cryptography
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Quantum Computing Overview
Quantum Computing at Microsoft Stephen Jordan • As of November 2018: 60 entries, 392 references • Major new primitives discovered only every few years. Simulation is a killer app for quantum computing with a solid theoretical foundation. Chemistry Materials Nuclear and Particle Physics Image: David Parker, U. Birmingham Image: CERN Many problems are out of reach even for exascale supercomputers but doable on quantum computers. Understanding biological Nitrogen fixation Intractable on classical supercomputers But a 200-qubit quantum computer will let us understand it Finding the ground state of Ferredoxin 퐹푒2푆2 Classical algorithm Quantum algorithm 2012 Quantum algorithm 2015 ! ~3000 ~1 INTRACTABLE YEARS DAY Research on quantum algorithms and software is essential! Quantum algorithm in high level language (Q#) Compiler Machine level instructions Microsoft Quantum Development Kit Quantum Visual Studio Local and cloud Extensive libraries, programming integration and quantum samples, and language debugging simulation documentation Developing quantum applications 1. Find quantum algorithm with quantum speedup 2. Confirm quantum speedup after implementing all oracles 3. Optimize code until runtime is short enough 4. Embed into specific hardware estimate runtime Simulating Quantum Field Theories: Classically Feynman diagrams Lattice methods Image: Encyclopedia of Physics Image: R. Babich et al. Break down at strong Good for binding energies. coupling or high precision Sign problem: real time dynamics, high fermion density. There’s room for exponential -
Key Concepts for Future QIS Learners Workshop Output Published Online May 13, 2020
Key Concepts for Future QIS Learners Workshop output published online May 13, 2020 Background and Overview On behalf of the Interagency Working Group on Workforce, Industry and Infrastructure, under the NSTC Subcommittee on Quantum Information Science (QIS), the National Science Foundation invited 25 researchers and educators to come together to deliberate on defining a core set of key concepts for future QIS learners that could provide a starting point for further curricular and educator development activities. The deliberative group included university and industry researchers, secondary school and college educators, and representatives from educational and professional organizations. The workshop participants focused on identifying concepts that could, with additional supporting resources, help prepare secondary school students to engage with QIS and provide possible pathways for broader public engagement. This workshop report identifies a set of nine Key Concepts. Each Concept is introduced with a concise overall statement, followed by a few important fundamentals. Connections to current and future technologies are included, providing relevance and context. The first Key Concept defines the field as a whole. Concepts 2-6 introduce ideas that are necessary for building an understanding of quantum information science and its applications. Concepts 7-9 provide short explanations of critical areas of study within QIS: quantum computing, quantum communication and quantum sensing. The Key Concepts are not intended to be an introductory guide to quantum information science, but rather provide a framework for future expansion and adaptation for students at different levels in computer science, mathematics, physics, and chemistry courses. As such, it is expected that educators and other community stakeholders may not yet have a working knowledge of content covered in the Key Concepts. -
Quantum Clustering Algorithms
Quantum Clustering Algorithms Esma A¨ımeur [email protected] Gilles Brassard [email protected] S´ebastien Gambs [email protected] Universit´ede Montr´eal, D´epartement d’informatique et de recherche op´erationnelle C.P. 6128, Succursale Centre-Ville, Montr´eal (Qu´ebec), H3C 3J7 Canada Abstract Multidisciplinary by nature, Quantum Information By the term “quantization”, we refer to the Processing (QIP) is at the crossroads of computer process of using quantum mechanics in order science, mathematics, physics and engineering. It con- to improve a classical algorithm, usually by cerns the implications of quantum mechanics for infor- making it go faster. In this paper, we initiate mation processing purposes (Nielsen & Chuang, 2000). the idea of quantizing clustering algorithms Quantum information is very different from its classi- by using variations on a celebrated quantum cal counterpart: it cannot be measured reliably and it algorithm due to Grover. After having intro- is disturbed by observation, but it can exist in a super- duced this novel approach to unsupervised position of classical states. Classical and quantum learning, we illustrate it with a quantized information can be used together to realize wonders version of three standard algorithms: divisive that are out of reach of classical information processing clustering, k-medians and an algorithm for alone, such as being able to factorize efficiently large the construction of a neighbourhood graph. numbers, with dramatic cryptographic consequences We obtain a significant speedup compared to (Shor, 1997), search in a unstructured database with a the classical approach. quadratic speedup compared to the best possible clas- sical algorithms (Grover, 1997) and allow two people to communicate in perfect secrecy under the nose of an 1. -
Quantum Computing: Principles and Applications
Journal of International Technology and Information Management Volume 29 Issue 2 Article 3 2020 Quantum Computing: Principles and Applications Yoshito Kanamori University of Alaska Anchorage, [email protected] Seong-Moo Yoo University of Alabama in Huntsville, [email protected] Follow this and additional works at: https://scholarworks.lib.csusb.edu/jitim Part of the Communication Technology and New Media Commons, Computer and Systems Architecture Commons, Information Security Commons, Management Information Systems Commons, Science and Technology Studies Commons, Technology and Innovation Commons, and the Theory and Algorithms Commons Recommended Citation Kanamori, Yoshito and Yoo, Seong-Moo (2020) "Quantum Computing: Principles and Applications," Journal of International Technology and Information Management: Vol. 29 : Iss. 2 , Article 3. Available at: https://scholarworks.lib.csusb.edu/jitim/vol29/iss2/3 This Article is brought to you for free and open access by CSUSB ScholarWorks. It has been accepted for inclusion in Journal of International Technology and Information Management by an authorized editor of CSUSB ScholarWorks. For more information, please contact [email protected]. Journal of International Technology and Information Management Volume 29, Number 2 2020 Quantum Computing: Principles and Applications Yoshito Kanamori (University of Alaska Anchorage) Seong-Moo Yoo (University of Alabama in Huntsville) ABSTRACT The development of quantum computers over the past few years is one of the most significant advancements in the history of quantum computing. D-Wave quantum computer has been available for more than eight years. IBM has made its quantum computer accessible via its cloud service. Also, Microsoft, Google, Intel, and NASA have been heavily investing in the development of quantum computers and their applications. -
Is Quantum Search Practical?
Q UANTUM C OMPUTING IS QUANTUM SEARCH PRACTICAL? Gauging a quantum algorithm’s practical significance requires weighing it against the best conventional techniques applied to useful instances of the same problem. The authors show that several commonly suggested applications of Grover’s quantum search algorithm fail to offer computational improvements over the best conventional algorithms. ome researchers suggest achieving mas- polynomial-time algorithm for number factoring sive speedups in computing by exploiting is known, and the security of the RSA code used quantum-mechanical effects such as su- on the Internet relies on this problem’s difficulty. perposition (quantum parallelism) and If a large and error-tolerant quantum computer Sentanglement.1 A quantum algorithm typically were available today, running Shor’s algorithm on consists of applying quantum gates to quantum it could compromise e-commerce. states, but because the input to the algorithm Lov Grover’s quantum search algorithm is also might be normal classical bits (or nonquantum), widely studied. It must compete with advanced it only affects the selection of quantum gates. Af- classical search techniques in applications that use ter all the gates are applied, quantum measure- parallel processing3 or exploit problem structure, ment is performed, producing the algorithm’s often implicitly. Despite its promise, though, it is nonquantum output. Deutsch’s algorithm, for in- by no means clear whether, or how soon, quantum stance, solves a certain artificial problem in fewer computing methods will offer better performance steps than any classical (nonquantum) algorithm, in useful applications.4 Traditional complexity and its relative speedup grows with the problem analysis of Grover’s algorithm doesn’t consider the size. -
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. -
SECURING CLOUDS – the QUANTUM WAY 1 Introduction
SECURING CLOUDS – THE QUANTUM WAY Marmik Pandya Department of Information Assurance Northeastern University Boston, USA 1 Introduction Quantum mechanics is the study of the small particles that make up the universe – for instance, atoms et al. At such a microscopic level, the laws of classical mechanics fail to explain most of the observed phenomenon. At such a state quantum properties exhibited by particles is quite noticeable. Before we move forward a basic question arises that, what are quantum properties? To answer this question, we'll look at the basis of quantum mechanics – The Heisenberg's Uncertainty Principle. The uncertainty principle states that the more precisely the position is determined, the less precisely the momentum is known in this instant, and vice versa. For instance, if you measure the position of an electron revolving around the nucleus an atom, you cannot accurately measure its velocity. If you measure the electron's velocity, you cannot accurately determine its position. Equation for Heisenberg's uncertainty principle: Where σx standard deviation of position, σx the standard deviation of momentum and ħ is the reduced Planck constant, h / 2π. In a practical scenario, this principle is applied to photons. Photons – the smallest measure of light, can exist in all of their possible states at once and also they don’t have any mass. This means that whatever direction a photon can spin in -- say, diagonally, vertically and horizontally -- it does all at once. This is exactly the same as if you constantly moved east, west, north, south, and up-and-down at the same time. -
Computational Complexity: a Modern Approach
i Computational Complexity: A Modern Approach Sanjeev Arora and Boaz Barak Princeton University http://www.cs.princeton.edu/theory/complexity/ [email protected] Not to be reproduced or distributed without the authors’ permission ii Chapter 10 Quantum Computation “Turning to quantum mechanics.... secret, secret, close the doors! we always have had a great deal of difficulty in understanding the world view that quantum mechanics represents ... It has not yet become obvious to me that there’s no real problem. I cannot define the real problem, therefore I suspect there’s no real problem, but I’m not sure there’s no real problem. So that’s why I like to investigate things.” Richard Feynman, 1964 “The only difference between a probabilistic classical world and the equations of the quantum world is that somehow or other it appears as if the probabilities would have to go negative..” Richard Feynman, in “Simulating physics with computers,” 1982 Quantum computing is a new computational model that may be physically realizable and may provide an exponential advantage over “classical” computational models such as prob- abilistic and deterministic Turing machines. In this chapter we survey the basic principles of quantum computation and some of the important algorithms in this model. One important reason to study quantum computers is that they pose a serious challenge to the strong Church-Turing thesis (see Section 1.6.3), which stipulates that every physi- cally reasonable computation device can be simulated by a Turing machine with at most polynomial slowdown. As we will see in Section 10.6, there is a polynomial-time algorithm for quantum computers to factor integers, whereas despite much effort, no such algorithm is known for deterministic or probabilistic Turing machines. -
On Counterfactual Computation
On Counterfactual Computation Paolo Zuliani Department of Computer Science Princeton University Princeton, NJ 08544, USA [email protected] Abstract In this paper we pursue two targets. First, showing that counterfactual computa- tion can be rigorously formalised as a quantum computation. Second, presenting a new counterfactual protocol which improve previous protocols. Counterfactual computation makes use of quantum mechanics' peculiarities to infer the outcome of a quantum com- putation without running that computation. In this paper, we first cast the definition of counterfactual protocol in the quantum programming language qGCL, thereby show- ing that counterfactual computation is an example of quantum computation. Next, we formalise in qGCL a probabilistic extension of counterfactual protocol for decision r problems (whose result is either 0 or 1). If pG denotes for protocol G the probability of obtaining result r \for free" (i.e. without running the quantum computer), then we 0 1 show that for any probabilistic protocol pG + pG ≤ 1 (as for non-probabilistic pro- 0 1 tocols). Finally, we present a probabilistic protocol K which satisfies pK + pK = 1, thus being optimal. Furthermore, the result is attained with a single insertion of the quantum computer, while it has been shown that a non-probabilistic protocol would obtain the result only in the limit (i.e. with an infinite number of insertions). 1 Introduction Counterfactuality is the fact that the sole possibility for an event to occur allows one to gain some information about that event, even though it did not actually occur. Counterfac- tual computation [4, 5] uses peculiar features of quantum mechanics to infer counterfactual statements about the result of a computation. -
Superconducting Transmon Machines
Quantum Computing Hardware Platforms: Superconducting Transmon Machines • CSC591/ECE592 – Quantum Computing • Fall 2020 • Professor Patrick Dreher • Research Professor, Department of Computer Science • Chief Scientist, NC State IBM Q Hub 3 November 2020 Superconducting Transmon Quantum Computers 1 5 November 2020 Patrick Dreher Outline • Introduction – Digital versus Quantum Computation • Conceptual design for superconducting transmon QC hardware platforms • Construction of qubits with electronic circuits • Superconductivity • Josephson junction and nonlinear LC circuits • Transmon design with example IBM chip layout • Working with a superconducting transmon hardware platform • Quantum Mechanics of Two State Systems - Rabi oscillations • Performance characteristics of superconducting transmon hardware • Construct the basic CNOT gate • Entangled transmons and controlled gates • Appendices • References • Quantum mechanical solution of the 2 level system 3 November 2020 Superconducting Transmon Quantum Computers 2 5 November 2020 Patrick Dreher Digital Computation Hardware Platforms Based on a Base2 Mathematics • Design principle for a digital computer is built on base2 mathematics • Identify voltage changes in electrical circuits that map base2 math into a “zero” or “one” corresponding to an on/off state • Build electrical circuits from simple on/off states that apply these basic rules to construct digital computers 3 November 2020 Superconducting Transmon Quantum Computers 3 5 November 2020 Patrick Dreher From Previous Lectures It was -
Quantum Distributed Computing Applied to Grover's Search Algorithm
Quantum Distributed Computing Applied to Grover’s Search Algorithm B Debabrata Goswami( ) Indian Institute of Technology Kanpur, Kanpur 208016, India [email protected] Abstract. Grover’s Algorithm√ finds a unique element in an unsorted stock of N-elements in N queries through quantum search. A single- query solution can also be designed, but with an overhead of N log2 N steps to prepare and post process the query, which is worse than the classical N/2 queries. We show here that by distributing the computing load on a set of quantum computers, we achieve better information the- oretic bounds and relaxed space scaling. Howsoever small one quantum computing node is, by virtue of networking and sharing of data, we can virtually work with a sufficiently large qubit space. Keywords: Distributed quantum computing · Grover’s quantum search · Optical networking 1 Introduction Today’s digital computer is the cumulation of technological advancements that began with the mechanical clockwork ideas of Charles Babbage in the nineteenth century. However, it is surprising that logically, the high speed modern day com- puter is not fundamentally different from its gigantic 30 ton ancestors, the first of which were built in 1941 by the German engineer Konrad Zuse. Although comput- ers nowadays have become more compact and considerably faster in their perfor- mance, their primary execution methodology has remained the same, which is to derive a computationally useful result via the manipulation and interpretation of encoded bits. The underlying mathematical principles are indistinguishable from those outlined in the visionary Church-Turing hypothesis, proposed in the year 1936, much ahead of the birth of the first computer. -
Arxiv:0905.2794V4 [Quant-Ph] 21 Jun 2013 Error Correction 7 B
Quantum Error Correction for Beginners Simon J. Devitt,1, ∗ William J. Munro,2 and Kae Nemoto1 1National Institute of Informatics 2-1-2 Hitotsubashi Chiyoda-ku Tokyo 101-8340. Japan 2NTT Basic Research Laboratories NTT Corporation 3-1 Morinosato-Wakamiya, Atsugi Kanagawa 243-0198. Japan (Dated: June 24, 2013) Quantum error correction (QEC) and fault-tolerant quantum computation represent one of the most vital theoretical aspect of quantum information processing. It was well known from the early developments of this exciting field that the fragility of coherent quantum systems would be a catastrophic obstacle to the development of large scale quantum computers. The introduction of quantum error correction in 1995 showed that active techniques could be employed to mitigate this fatal problem. However, quantum error correction and fault-tolerant computation is now a much larger field and many new codes, techniques, and methodologies have been developed to implement error correction for large scale quantum algorithms. In response, we have attempted to summarize the basic aspects of quantum error correction and fault-tolerance, not as a detailed guide, but rather as a basic introduction. This development in this area has been so pronounced that many in the field of quantum information, specifically researchers who are new to quantum information or people focused on the many other important issues in quantum computation, have found it difficult to keep up with the general formalisms and methodologies employed in this area. Rather than introducing these concepts from a rigorous mathematical and computer science framework, we instead examine error correction and fault-tolerance largely through detailed examples, which are more relevant to experimentalists today and in the near future.