Qudits and High-Dimensional Quantum Computing
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Modern Quantum Technologies of Information Security
MODERN QUANTUM TECHNOLOGIES OF INFORMATION SECURITY Oleksandr Korchenko 1, Yevhen Vasiliu 2, Sergiy Gnatyuk 3 1,3 Dept of Information Security Technologies, National Aviation University, Kosmonavta Komarova Ave 1, 03680 Kyiv, Ukraine 2Dept of Information Technologies and Control Systems, Odesa National Academy of Telecommunications n.a. O.S. Popov, Koval`ska Str 1, 65029 Odesa, Ukraine E-mails: [email protected], [email protected], [email protected] In this paper, the systematisation and classification of modern quantum technologies of information security against cyber-terrorist attack are carried out. The characteristic of the basic directions of quantum cryptography from the viewpoint of the quantum technologies used is given. A qualitative analysis of the advantages and disadvantages of concrete quantum protocols is made. The current status of the problem of practical quantum cryptography use in telecommunication networks is considered. In particular, a short review of existing commercial systems of quantum key distribution is given. 1. Introduction Today there is virtually no area where information technology ( ІТ ) is not used in some way. Computers support banking systems, control the work of nuclear reactors, and control aircraft, satellites and spacecraft. The high level of automation therefore depends on the security level of IT. The latest achievements in communication systems are now applied in aviation. These achievements are public switched telephone network (PSTN), circuit switched public data network (CSPDN), packet switched public data network (PSPDN), local area network (LAN), and integrated services digital network (ISDN) [73]. These technologies provide data transmission systems of various types: surface-to-surface, surface-to-air, air-to-air, and space telecommunication. -
Simulating Quantum Field Theory with a Quantum Computer
Simulating quantum field theory with a quantum computer John Preskill Lattice 2018 28 July 2018 This talk has two parts (1) Near-term prospects for quantum computing. (2) Opportunities in quantum simulation of quantum field theory. Exascale digital computers will advance our knowledge of QCD, but some challenges will remain, especially concerning real-time evolution and properties of nuclear matter and quark-gluon plasma at nonzero temperature and chemical potential. Digital computers may never be able to address these (and other) problems; quantum computers will solve them eventually, though I’m not sure when. The physics payoff may still be far away, but today’s research can hasten the arrival of a new era in which quantum simulation fuels progress in fundamental physics. Frontiers of Physics short distance long distance complexity Higgs boson Large scale structure “More is different” Neutrino masses Cosmic microwave Many-body entanglement background Supersymmetry Phases of quantum Dark matter matter Quantum gravity Dark energy Quantum computing String theory Gravitational waves Quantum spacetime particle collision molecular chemistry entangled electrons A quantum computer can simulate efficiently any physical process that occurs in Nature. (Maybe. We don’t actually know for sure.) superconductor black hole early universe Two fundamental ideas (1) Quantum complexity Why we think quantum computing is powerful. (2) Quantum error correction Why we think quantum computing is scalable. A complete description of a typical quantum state of just 300 qubits requires more bits than the number of atoms in the visible universe. Why we think quantum computing is powerful We know examples of problems that can be solved efficiently by a quantum computer, where we believe the problems are hard for classical computers. -
Quantum Information in the Posner Model of Quantum Cognition
Quantum information in quantum cognition Nicole Yunger Halpern1, 2 and Elizabeth Crosson1 1Institute for Quantum Information and Matter, Caltech, Pasadena, CA 91125, USA 2Kavli Institute for Theoretical Physics, University of California, Santa Barbara, CA 93106, USA (Dated: November 15, 2017) Matthew Fisher recently postulated a mechanism by which quantum phenomena could influence cognition: Phosphorus nuclear spins may resist decoherence for long times. The spins would serve as biological qubits. The qubits may resist decoherence longer when in Posner molecules. We imagine that Fisher postulates correctly. How adroitly could biological systems process quantum information (QI)? We establish a framework for answering. Additionally, we apply biological qubits in quantum error correction, quantum communication, and quantum computation. First, we posit how the QI encoded by the spins transforms as Posner molecules form. The transformation points to a natural computational basis for qubits in Posner molecules. From the basis, we construct a quantum code that detects arbitrary single-qubit errors. Each molecule encodes one qutrit. Shifting from information storage to computation, we define the model of Posner quantum computation. To illustrate the model's quantum-communication ability, we show how it can teleport information in- coherently: A state's weights are teleported; the coherences are not. The dephasing results from the entangling operation's simulation of a coarse-grained Bell measurement. Whether Posner quantum computation is universal remains an open question. However, the model's operations can efficiently prepare a Posner state usable as a resource in universal measurement-based quantum computation. The state results from deforming the Affleck-Lieb-Kennedy-Tasaki (AKLT) state and is a projected entangled-pair state (PEPS). -
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. -
Parafermions in a Kagome Lattice of Qubits for Topological Quantum Computation
PHYSICAL REVIEW X 5, 041040 (2015) Parafermions in a Kagome Lattice of Qubits for Topological Quantum Computation Adrian Hutter, James R. Wootton, and Daniel Loss Department of Physics, University of Basel, Klingelbergstrasse 82, CH-4056 Basel, Switzerland (Received 3 June 2015; revised manuscript received 16 September 2015; published 14 December 2015) Engineering complex non-Abelian anyon models with simple physical systems is crucial for topological quantum computation. Unfortunately, the simplest systems are typically restricted to Majorana zero modes (Ising anyons). Here, we go beyond this barrier, showing that the Z4 parafermion model of non-Abelian anyons can be realized on a qubit lattice. Our system additionally contains the Abelian DðZ4Þ anyons as low-energetic excitations. We show that braiding of these parafermions with each other and with the DðZ4Þ anyons allows the entire d ¼ 4 Clifford group to be generated. The error-correction problem for our model is also studied in detail, guaranteeing fault tolerance of the topological operations. Crucially, since the non- Abelian anyons are engineered through defect lines rather than as excitations, non-Abelian error correction is not required. Instead, the error-correction problem is performed on the underlying Abelian model, allowing high noise thresholds to be realized. DOI: 10.1103/PhysRevX.5.041040 Subject Areas: Condensed Matter Physics, Quantum Physics, Quantum Information I. INTRODUCTION Non-Abelian anyons supported by a qubit system Non-Abelian anyons exhibit exotic physics that would typically are Majorana zero modes, also known as Ising – make them an ideal basis for topological quantum compu- anyons [12 15]. A variety of proposals for experimental tation [1–3]. -
A Scanning Transmon Qubit for Strong Coupling Circuit Quantum Electrodynamics
ARTICLE Received 8 Mar 2013 | Accepted 10 May 2013 | Published 7 Jun 2013 DOI: 10.1038/ncomms2991 A scanning transmon qubit for strong coupling circuit quantum electrodynamics W. E. Shanks1, D. L. Underwood1 & A. A. Houck1 Like a quantum computer designed for a particular class of problems, a quantum simulator enables quantitative modelling of quantum systems that is computationally intractable with a classical computer. Superconducting circuits have recently been investigated as an alternative system in which microwave photons confined to a lattice of coupled resonators act as the particles under study, with qubits coupled to the resonators producing effective photon–photon interactions. Such a system promises insight into the non-equilibrium physics of interacting bosons, but new tools are needed to understand this complex behaviour. Here we demonstrate the operation of a scanning transmon qubit and propose its use as a local probe of photon number within a superconducting resonator lattice. We map the coupling strength of the qubit to a resonator on a separate chip and show that the system reaches the strong coupling regime over a wide scanning area. 1 Department of Electrical Engineering, Princeton University, Olden Street, Princeton 08550, New Jersey, USA. Correspondence and requests for materials should be addressed to W.E.S. (email: [email protected]). NATURE COMMUNICATIONS | 4:1991 | DOI: 10.1038/ncomms2991 | www.nature.com/naturecommunications 1 & 2013 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms2991 ver the past decade, the study of quantum physics using In this work, we describe a scanning superconducting superconducting circuits has seen rapid advances in qubit and demonstrate its coupling to a superconducting CPWR Osample design and measurement techniques1–3. -
Arxiv:1807.01863V1 [Quant-Ph] 5 Jul 2018 Β 2| +I Γ 2 =| I 1
Quantum Error Correcting Code for Ternary Logic Ritajit Majumdar1,∗ Saikat Basu2, Shibashis Ghosh2, and Susmita Sur-Kolay1y 1Advanced Computing & Microelectronics Unit, Indian Statistical Institute, India 2A. K. Choudhury School of Information Technology, University of Calcutta, India Ternary quantum systems are being studied because these provide more computational state space per unit of information, known as qutrit. A qutrit has three basis states, thus a qubit may be considered as a special case of a qutrit where the coefficient of one of the basis states is zero. Hence both (2 × 2)-dimensional as well as (3 × 3)-dimensional Pauli errors can occur on qutrits. In this paper, we (i) explore the possible (2 × 2)-dimensional as well as (3 × 3)-dimensional Pauli errors in qutrits and show that any pairwise bit swap error can be expressed as a linear combination of shift errors and phase errors, (ii) propose a new type of error called quantum superposition error and show its equivalence to arbitrary rotation, (iii) formulate a nine qutrit code which can correct a single error in a qutrit, and (iv) provide its stabilizer and circuit realization. I. INTRODUCTION errors or (d d)-dimensional errors only and no explicit circuit has been× presented. Quantum computers hold the promise of reducing the Main Contributions: In this paper, we study error cor- computational complexity of certain problems. However, rection in qutrits considering both (2 2)-dimensional × quantum systems are highly sensitive to errors; even in- as well as (3 3)-dimensional errors. We have intro- × teraction with environment can cause a change of state. -
Quantum Entanglement Concentration Based on Nonlinear Optics for Quantum Communications
Entropy 2013, 15, 1776-1820; doi:10.3390/e15051776 OPEN ACCESS entropy ISSN 1099-4300 www.mdpi.com/journal/entropy Review Quantum Entanglement Concentration Based on Nonlinear Optics for Quantum Communications Yu-Bo Sheng 1;2;* and Lan Zhou 2;3 1 Institute of Signal Processing Transmission, Nanjing University of Posts and Telecommunications, Nanjing 210003, China 2 Key Lab of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing 210003, China 3 College of Mathematics & Physics, Nanjing University of Posts and Telecommunications, Nanjing 210003, China; E-Mail: [email protected] (L.Z.) * Author to whom correspondence should be addressed; E-Mail: [email protected]; Tel./Fax: +86-025-83492417. Received: 14 March 2013; in revised form: 3 May 2013 / Accepted: 8 May 2013 / Published: 16 May 2013 Abstract: Entanglement concentration is of most importance in long distance quantum communication and quantum computation. It is to distill maximally entangled states from pure partially entangled states based on the local operation and classical communication. In this review, we will mainly describe two kinds of entanglement concentration protocols. One is to concentrate the partially entangled Bell-state, and the other is to concentrate the partially entangled W state. Some protocols are feasible in current experimental conditions and suitable for the optical, electric and quantum-dot and optical microcavity systems. Keywords: quantum entanglement; entanglement concentration; quantum communication 1. Introduction Quantum communication and quantum computation have attracted much attention over the last 20 years, due to the absolute safety in the information transmission for quantum communication and the super fast factoring for quantum computation [1,2]. -
Clustering by Quantum Annealing on the Three–Level Quantum El- Ements Qutrits V
CLUSTERING BY QUANTUM ANNEALING ON THE THREE–LEVEL QUANTUM EL- EMENTS QUTRITS V. E. Zobov and I. S. Pichkovskiy1 Abstract Clustering is grouping of data by the proximity of some properties. We report on the possibility of increasing the efficiency of clustering of points in a plane using artificial quantum neural networks after the replacement of the two-level neurons called qubits represented by the spins S = 1/2 by the three-level neu- rons called qutrits represented by the spins S = 1. The problem has been solved by the slow adiabatic change of the Hamiltonian in time. The methods for controlling a qutrit system using projection operators have been developed and the numerical simulation has been performed. The Hamiltonians for two well-known cluster- ing methods, one-hot encoding and k-means ++, have been built. The first method has been used to partition a set of six points into three or two clusters and the second method, to partition a set of nine points into three clusters and seven points into four clusters. The simulation has shown that the clustering problem can be ef- fectively solved on qutrits represented by the spins S = 1. The advantages of clustering on qutrits over that on qubits have been demonstrated. In particular, the number of qutrits required to represent N data points is smaller than the number of qubits by a factor of log23NN / log . Since, for qutrits, it is easier to partition the data points into three clusters rather than two ones, the approximate hierarchical procedure of data partition- ing into a larger number of clusters is accelerated. -
An Introduction to Quantum Computing
An Introduction to Quantum Computing CERN Bo Ewald October 17, 2017 Bo Ewald November 5, 2018 TOPICS •Introduction to Quantum Computing • Introduction and Background • Quantum Annealing •Early Applications • Optimization • Machine Learning • Material Science • Cybersecurity • Fiction •Final Thoughts Copyright © D-Wave Systems Inc. 2 Richard Feynman – Proposed Quantum Computer in 1981 1960 1970 1980 1990 2000 2010 2020 Copyright © D-Wave Systems Inc. 3 April 1983 – Richard Feynman’s Talk at Los Alamos Title: Los Alamos Experience Author: Phyllis K Fisher Page 247 Copyright © D-Wave Systems Inc. 4 The “Marriage” Between Technology and Architecture • To design and build any computer, one must select a compatible technology and a system architecture • Technology – the physical devices (IC’s, PCB’s, interconnects, etc) used to implement the hardware • Architecture – the organization and rules that govern how the computer will operate • Digital – CISC (Intel x86), RISC (MIPS, SPARC), Vector (Cray), SIMD (CM-1), Volta (NVIDIA) • Quantum – Gate or Circuit (IBM, Rigetti) Annealing (D-Wave, ARPA QEO) Copyright © D-Wave Systems Inc. 5 Quantum Technology – “qubit” Building Blocks Copyright © D-Wave Systems Inc. 6 Simulation on IBM Quantum Experience (IBM QX) Preparation Rotation by Readout of singlet state 휃1 and 휃2 measurement IBM QX, Yorktown Heights, USA X X-gate: U1 phase-gate: Xȁ0ۧ = ȁ1ۧ U1ȁ0ۧ = ȁ0ۧ, U1ȁ1ۧ = 푒푖휃ȁ1ۧ Xȁ1ۧ = ȁ0ۧ ۧ CNOT gate: C ȁ0 0 ۧ = ȁ0 0 Hadamard gate: 01 1 0 1 0 H C01ȁ0110ۧ = ȁ1110ۧ Hȁ0ۧ = ȁ0ۧ + ȁ1ۧ / 2 C01ȁ1100ۧ = ȁ1100ۧ + Hȁ1ۧ = ȁ0ۧ − ȁ1ۧ / 2 C01ȁ1110ۧ = ȁ0110ۧ 7 Simulated Annealing on Digital Computers 1950 1960 1970 1980 1990 2000 2010 Copyright © D-Wave Systems Inc. -
Simulating Quantum Field Theory with a Quantum Computer
Simulating quantum field theory with a quantum computer John Preskill Bethe Lecture, Cornell 12 April 2019 Frontiers of Physics short distance long distance complexity Higgs boson Large scale structure “More is different” Neutrino masses Cosmic microwave Many-body entanglement background Supersymmetry Phases of quantum Dark matter matter Quantum gravity Dark energy Quantum computing String theory Gravitational waves Quantum spacetime particle collision molecular chemistry entangled electrons A quantum computer can simulate efficiently any physical process that occurs in Nature. (Maybe. We don’t actually know for sure.) superconductor black hole early universe Opportunities in quantum simulation of quantum field theory Exascale digital computers will advance our knowledge of QCD, but some challenges will remain, especially concerning real-time evolution and properties of nuclear matter and quark-gluon plasma at nonzero temperature and chemical potential. Digital computers may never be able to address these (and other) problems; quantum computers will solve them eventually, though I’m not sure when. The physics payoff may still be far away, but today’s research can hasten the arrival of a new era in which quantum simulation fuels progress in fundamental physics. Collaborators: Stephen Jordan, Keith Lee, Hari Krovi arXiv: 1111.3633, 1112.4833, 1404.7115, 1703.00454, 1811.10085 Work in progress: Alex Buser, Junyu Liu, Burak Sahinoglu ??? Quantum Supremacy! Quantum computing in the NISQ Era The (noisy) 50-100 qubit quantum computer is coming soon. (NISQ = noisy intermediate-scale quantum .) NISQ devices cannot be simulated by brute force using the most powerful currently existing supercomputers. Noise limits the computational power of NISQ-era technology. -
Using Machine Learning for Quantum Annealing Accuracy Prediction
algorithms Article Using Machine Learning for Quantum Annealing Accuracy Prediction Aaron Barbosa 1, Elijah Pelofske 1, Georg Hahn 2,* and Hristo N. Djidjev 1,3 1 CCS-3 Information Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA; [email protected] (A.B.); [email protected] (E.P.); [email protected] (H.N.D.) 2 T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA 3 Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, 1113 Sofia, Bulgaria * Correspondence: [email protected] Abstract: Quantum annealers, such as the device built by D-Wave Systems, Inc., offer a way to compute solutions of NP-hard problems that can be expressed in Ising or quadratic unconstrained binary optimization (QUBO) form. Although such solutions are typically of very high quality, problem instances are usually not solved to optimality due to imperfections of the current generations quantum annealers. In this contribution, we aim to understand some of the factors contributing to the hardness of a problem instance, and to use machine learning models to predict the accuracy of the D-Wave 2000Q annealer for solving specific problems. We focus on the maximum clique problem, a classic NP-hard problem with important applications in network analysis, bioinformatics, and computational chemistry. By training a machine learning classification model on basic problem characteristics such as the number of edges in the graph, or annealing parameters, such as the D-Wave’s chain strength, we are able to rank certain features in the order of their contribution to the solution hardness, and present a simple decision tree which allows to predict whether a problem Citation: Barbosa, A.; Pelofske, E.; will be solvable to optimality with the D-Wave 2000Q.