Realisation of Qudits in Coupled Potential Wells Ariel Landau Tel Aviv University
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Engineering the Coupling of Superconducting Qubits
Engineering the coupling of superconducting qubits Von der Fakultät für Mathematik, Informatik und Naturwissenschaften der RWTH Aachen University zur Erlangung des akademischen Grades eines Doktors der Naturwissenschaften genehmigte Dissertation vorgelegt von M. Sc. Alessandro Ciani aus Penne, Italien Berichter: Prof. Dr. David DiVincenzo Prof. Dr. Fabian Hassler Tag der mündlichen Prüfung: 12 April 2019 Diese Dissertation ist auf den Internetseiten der Universitätsbibliothek verfügbar. ii «Considerate la vostra semenza: fatti non foste a viver come bruti, ma per seguir virtute e conoscenza.» Dante Alighieri, "La Divina Commedia", Inferno, Canto XXVI, vv. 118-120. iii Abstract The way to build a scalable and reliable quantum computer that truly exploits the quantum power faces several challenges. Among the various proposals for building a quantum computer, superconducting qubits have rapidly progressed and hold good promises in the near-term future. In particular, the possibility to design the required interactions is one of the most appealing features of this kind of architecture. This thesis deals with some detailed aspects of this problem focusing on architectures based on superconducting transmon-like qubits. After reviewing the basic tools needed for the study of superconducting circuits and the main kinds of superconducting qubits, we move to the analyisis of a possible scheme for realizing direct parity measurement. Parity measurements, or in general stabilizer measurements, are fundamental tools for realizing quantum error correct- ing codes, that are believed to be fundamental for dealing with the problem of de- coherence that affects any physical implementation of a quantum computer. While these measurements are usually done indirectly with the help of ancilla qubits, the scheme that we analyze performs the measurement directly, and requires the engi- neering of a precise matching condition. -
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 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. -
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. -
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. -
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. -
Control of the Geometric Phase in Two Open Qubit–Cavity Systems Linked by a Waveguide
entropy Article Control of the Geometric Phase in Two Open Qubit–Cavity Systems Linked by a Waveguide Abdel-Baset A. Mohamed 1,2 and Ibtisam Masmali 3,* 1 Department of Mathematics, College of Science and Humanities, Prince Sattam bin Abdulaziz University, Al-Aflaj 710-11912, Saudi Arabia; [email protected] 2 Faculty of Science, Assiut University, Assiut 71516, Egypt 3 Department of Mathematics, Faculty of Science, Jazan University, Gizan 82785, Saudi Arabia * Correspondence: [email protected] Received: 28 November 2019; Accepted: 8 January 2020; Published: 10 January 2020 Abstract: We explore the geometric phase in a system of two non-interacting qubits embedded in two separated open cavities linked via an optical fiber and leaking photons to the external environment. The dynamical behavior of the generated geometric phase is investigated under the physical parameter effects of the coupling constants of both the qubit–cavity and the fiber–cavity interactions, the resonance/off-resonance qubit–field interactions, and the cavity dissipations. It is found that these the physical parameters lead to generating, disappearing and controlling the number and the shape (instantaneous/rectangular) of the geometric phase oscillations. Keywords: geometric phase; cavity damping; optical fiber 1. Introduction The mathematical manipulations of the open quantum systems, of the qubit–field interactions, depend on the ability of solving the master-damping [1] and intrinsic-decoherence [2] equations, analytically/numerically. To remedy the problems of these manipulations, the quantum phenomena of the open systems were studied for limited physical circumstances [3–7]. The quantum geometric phase is a basic intrinsic feature in quantum mechanics that is used as the basis of quantum computation [8]. -
Thermally Assisted Quantum Annealing of a 16-Qubit Problem
ARTICLE Received 29 Jan 2013 | Accepted 23 Apr 2013 | Published 21 May 2013 DOI: 10.1038/ncomms2920 Thermally assisted quantum annealing of a 16-qubit problem N. G. Dickson1, M. W. Johnson1, M. H. Amin1,2, R. Harris1, F. Altomare1, A. J. Berkley1, P. Bunyk1, J. Cai1, E. M. Chapple1, P. Chavez1, F. Cioata1, T. Cirip1, P. deBuen1, M. Drew-Brook1, C. Enderud1, S. Gildert1, F. Hamze1, J. P. Hilton1, E. Hoskinson1, K. Karimi1, E. Ladizinsky1, N. Ladizinsky1, T. Lanting1, T. Mahon1, R. Neufeld1,T.Oh1, I. Perminov1, C. Petroff1, A. Przybysz1, C. Rich1, P. Spear1, A. Tcaciuc1, M. C. Thom1, E. Tolkacheva1, S. Uchaikin1, J. Wang1, A. B. Wilson1, Z. Merali3 &G.Rose1 Efforts to develop useful quantum computers have been blocked primarily by environmental noise. Quantum annealing is a scheme of quantum computation that is predicted to be more robust against noise, because despite the thermal environment mixing the system’s state in the energy basis, the system partially retains coherence in the computational basis, and hence is able to establish well-defined eigenstates. Here we examine the environment’s effect on quantum annealing using 16 qubits of a superconducting quantum processor. For a problem instance with an isolated small-gap anticrossing between the lowest two energy levels, we experimentally demonstrate that, even with annealing times eight orders of magnitude longer than the predicted single-qubit decoherence time, the probabilities of performing a successful computation are similar to those expected for a fully coherent system. Moreover, for the problem studied, we show that quantum annealing can take advantage of a thermal environment to achieve a speedup factor of up to 1,000 over a closed system. -
An Introduction to Quantum Annealing
RAIRO-Theor. Inf. Appl. 45 (2011) 99–116 Available online at: DOI: 10.1051/ita/2011013 www.rairo-ita.org AN INTRODUCTION TO QUANTUM ANNEALING Diego de Falco1 and Dario Tamascelli1 Abstract. Quantum annealing, or quantum stochastic optimization, is a classical randomized algorithm which provides good heuristics for the solution of hard optimization problems. The algorithm, suggested by the behaviour of quantum systems, is an example of proficuous cross contamination between classical and quantum computer science. In this survey paper we illustrate how hard combinatorial problems are tackled by quantum computation and present some examples of the heuristics provided by quantum annealing. We also present preliminary results about the application of quantum dissipation (as an alternative to imaginary time evolution) to the task of driving a quantum system toward its state of lowest energy. Mathematics Subject Classification. 81P68, 68Q12, 68W25. 1. Introduction Quantum computation stems from a simple observation [19,23]: any compu- tation is ultimately performed on a physical device; to any input-output relation there must correspond a change in the state of the device. If the device is micro- scopic, then its evolution is ruled by the laws of quantum mechanics. The question of whether the change of evolution rules can lead to a breakthrough in computational complexity theory is still unanswered [11,34,40]. What is known is that a quantum computer would outperform a classical one in specific tasks such as integer factorization [47] and searching an unordered database [28,29]. In fact, Grover’s algorithm can search a keyword quadratically faster than any classical algorithm, whereas in the case of Shor’s factorization the speedup is exponential. -
Physical Implementations of Quantum Computing
Physical implementations of quantum computing Andrew Daley Department of Physics and Astronomy University of Pittsburgh Overview (Review) Introduction • DiVincenzo Criteria • Characterising coherence times Survey of possible qubits and implementations • Neutral atoms • Trapped ions • Colour centres (e.g., NV-centers in diamond) • Electron spins (e.g,. quantum dots) • Superconducting qubits (charge, phase, flux) • NMR • Optical qubits • Topological qubits Back to the DiVincenzo Criteria: Requirements for the implementation of quantum computation 1. A scalable physical system with well characterized qubits 1 | i 0 | i 2. The ability to initialize the state of the qubits to a simple fiducial state, such as |000...⟩ 1 | i 0 | i 3. Long relevant decoherence times, much longer than the gate operation time 4. A “universal” set of quantum gates control target (single qubit rotations + C-Not / C-Phase / .... ) U U 5. A qubit-specific measurement capability D. P. DiVincenzo “The Physical Implementation of Quantum Computation”, Fortschritte der Physik 48, p. 771 (2000) arXiv:quant-ph/0002077 Neutral atoms Advantages: • Production of large quantum registers • Massive parallelism in gate operations • Long coherence times (>20s) Difficulties: • Gates typically slower than other implementations (~ms for collisional gates) (Rydberg gates can be somewhat faster) • Individual addressing (but recently achieved) Quantum Register with neutral atoms in an optical lattice 0 1 | | Requirements: • Long lived storage of qubits • Addressing of individual qubits • Single and two-qubit gate operations • Array of singly occupied sites • Qubits encoded in long-lived internal states (alkali atoms - electronic states, e.g., hyperfine) • Single-qubit via laser/RF field coupling • Entanglement via Rydberg gates or via controlled collisions in a spin-dependent lattice Rb: Group II Atoms 87Sr (I=9/2): Extensively developed, 1 • P1 e.g., optical clocks 3 • Degenerate gases of Yb, Ca,.. -
Magnetically Induced Transparency of a Quantum Metamaterial Composed of Twin flux Qubits
ARTICLE DOI: 10.1038/s41467-017-02608-8 OPEN Magnetically induced transparency of a quantum metamaterial composed of twin flux qubits K.V. Shulga 1,2,3,E.Il’ichev4, M.V. Fistul 2,5, I.S. Besedin2, S. Butz1, O.V. Astafiev2,3,6, U. Hübner 4 & A.V. Ustinov1,2 Quantum theory is expected to govern the electromagnetic properties of a quantum metamaterial, an artificially fabricated medium composed of many quantum objects acting as 1234567890():,; artificial atoms. Propagation of electromagnetic waves through such a medium is accompanied by excitations of intrinsic quantum transitions within individual meta-atoms and modes corresponding to the interactions between them. Here we demonstrate an experiment in which an array of double-loop type superconducting flux qubits is embedded into a microwave transmission line. We observe that in a broad frequency range the transmission coefficient through the metamaterial periodically depends on externally applied magnetic field. Field-controlled switching of the ground state of the meta-atoms induces a large suppression of the transmission. Moreover, the excitation of meta-atoms in the array leads to a large resonant enhancement of the transmission. We anticipate possible applications of the observed frequency-tunable transparency in superconducting quantum networks. 1 Physikalisches Institut, Karlsruhe Institute of Technology, D-76131 Karlsruhe, Germany. 2 Russian Quantum Center, National University of Science and Technology MISIS, Moscow, 119049, Russia. 3 Moscow Institute of Physics and Technology, Dolgoprudny, 141700 Moscow region, Russia. 4 Leibniz Institute of Photonic Technology, PO Box 100239, D-07702 Jena, Germany. 5 Center for Theoretical Physics of Complex Systems, Institute for Basic Science, Daejeon, 34051, Republic of Korea.