DOCSLIB.ORG

Thermally Assisted Quantum Annealing of a 16-Qubit Problem

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
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. 1 D-Wave Systems Inc., 100-4401 Still Creek Drive, Burnaby, British Columbia, Canada V5C 6G9. 2 Department of Physics, Simon Fraser University, Burnaby, British Columbia, Canada V5A 1S6. 3 Foundational Questions Institute, PO Box 3022, New York, New York 10163, USA. Correspondence and requests for materials should be addressed to M.H.A. (email: [email protected]). NATURE COMMUNICATIONS | 4:1903 | DOI: 10.1038/ncomms2920 | www.nature.com/naturecommunications 1 & 2013 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms2920 uantum computation1 is a computational paradigm that states, encountered at some point s ¼ s* known as an anticrossing harnesses quantum physics to solve problems. Theory (illustrated in Fig. 1). Using a two-state approximation, one can Qhas suggested that quantum computation could provide a write25,26,9: significant performance advantage over classical computation. À tf =ta 2 However, efforts to develop useful quantum computers have PGM 1 À e ; ta ¼ 2hn=pgmin; ð4Þ been blocked primarily due to environmental noise. Adiabatic where n ¼ |d(E1 À E0)/ds|s* is the relative slope of the energy levels quantum computation (AQC)2,3 is a scheme of quantum near s* and ta is the adiabatic timescale, which marks the computation that is theoretically predicted to be more robust 4–12 boundary between adiabatic (tf44ta) and non-adiabatic (tfoota) against noise . evolutions. Annealing such a closed system k times, with In AQC, a physical quantum system is initially prepared tf ¼ ttotal/k, yields the same Ptotal as annealing once with tf ¼ ttotal. in its known lowest-energy configuration, or ground state. The computation involves gradually deforming the system’s energy function, or Hamiltonian, in such a way that the system Open-system dynamics. In reality, all implementations of remains in the ground state throughout the evolution with high quantum algorithms are open systems, and thus subject to probability, and the ground state of the final Hamiltonian relaxation (transitions between energy eigenstates) and dephasing provides the solution to the problem to be solved. Quantum (randomization of relative phases between the eigenstates). In the annealing (QA)13–16 is a very similar, but more practical, scheme limit of weak coupling to the environment, dephasing in the of quantum computation that is different from AQC in two energy basis is irrelevant for QA, because only the ground-state aspects: the evolution is not required to be adiabatic, that is, probability matters and the relative phases between the energy the system may leave the ground state due to thermal or eigenstates do not carry any information during the computation. non-adiabatic transitions; and the final Hamiltonian is restricted Thermal excitation and relaxation, on the other hand, can reduce to be diagonal in the computation basis, with a ground state the instantaneous probability of the ground state by populating representing the solution to a hard optimization problem. Such the excited states. Nevertheless, in the limit of slow evolution, the optimization problems are integral to a wide range of applications ground state will always have the dominant probability, because from anthropology17 to zoology18. the excited states are occupied approximately with equilibrium QA has been experimentally demonstrated in non-program- (Boltzmann) probabilities. Unless there are an exponential 19,20 mable bulk solid state systems and also using 3-qubit nuclear number of states within the energy kBT (kB is Boltzmann’s magnetic resonance21. Recently, we have designed, fabricated and constant) from the ground state, the thermal reduction of the tested a programmable superconducting QA processor22,23. Here ground-state probability will not significantly affect the perfor- we report the first experimental exploration of the effect of mance. This picture changes in the strong coupling limit wherein thermal noise on QA. We explored this using a 16-qubit subset of it may no longer be possible to identify well-defined eigenstates of the processor used in Johnson et al.23 the system Hamiltonian independent of the environment27. It should be noted that, even if the environment is weakly coupled Results to the system and the equilibrium probability of the ground state Closed-system dynamics. Our processor implements the is not vanishingly small, the time to reach such a probability is a Hamiltonian concern for practical computation. For example, it is conceivable X that, due to thermal relaxation, one must wait orders of 1 x 1 HðsÞ¼ À DiðsÞsi þ EðsÞHP; ð1Þ magnitude longer than a typical closed-system evolution time 2 i 2 X X z z z HP ¼ hisi þ Jijsi sj ; ð2Þ i i o j A x;z where st/tf [0, 1], t is time, tf is the anneal time, si are Pauli |b 〉 δ 〉 matrices for the ith qubit, and Di(s) and E(s) are time-dependent E |a D 44 E energy scales. At t ¼ 0, i(0) E(0) 0, and the ground state is a Thermal Thermal superposition of all computation basis states, that is, all eigen- excitation relaxation z functions of s operators. At t ¼ t , E(1)44D (1)E0, and H(s)is Energy i f i gmin dominated by the Ising Hamiltonian HP, characterized by |a 〉 |b 〉 dimensionless local biases hi and pairwise couplings Jij. The Time ground state of H(1) thus represents the global minimum of HP. Finding this global minimum is known to be an NP-hard (non- Figure 1 | Illustration of thermal noise effects near an anticrossing. 24 deterministic polynomial time hard) optimization problem . An avoided crossing (anticrossing) of two lowest-energy eigenstates, An important feature of optimization problems is that, jia and jib , with a small minimum energy gap, gmin, separated from other although it may be challenging to find a global minimum, it is eigenstates by energy dE. Passing through the anticrossing very quickly easy to select the minimum from a set of candidate solutions, for swaps the probabilities of the ground and first excited states (blue and example, provided by a probabilistic algorithm such as QA. If one green dotted arrows), leaving the probabilities of jia and jib unchanged. is able to find a global minimum with probability PGM40 in one Thus, for a closed-system starting in jia , the final ground-state probability, trial taking time tf, the probability Ptotal of observing a global Pb, would be vanishingly small. However, with an environment at T40, minimum at least once in k-independent trials, and the total time thermal transitions can excite the system beforehand and relax it afterward required ttotal, is given by (red arrows), the net effect of which is to increase Pb (green arrow). Single-qubit tunnelling amplitudes are significantly larger before the P ¼ 1 Àð1 À P Þk; t ¼ kt ð3Þ total GM total f anticrossing (see Fig. 2b), making thermal excitations earlier in the annealing process much more likely than relaxation later. If T\dE/kB, For a noise-free (closed) system, PGM would depend on the higher excited states would also be occupied, reducing Pb; so a peak in B minimum energy gap, gmin, between the ground and first excited Pb is expected at T ¼ Tpeak dE/kB. 2 NATURE COMMUNICATIONS | 4:1903 | DOI: 10.1038/ncomms2920 | www.nature.com/naturecommunications & 2013 Macmillan Publishers Limited. All rights reserved. NATURE COMMUNICATIONS | DOI: 10.1038/ncomms2920 ARTICLE to reach an acceptable open-system probability. In that case, a despite the above argument, the computation cannot be hi = +1 considered robust against the environment. We therefore define h = 0 robustness against environmental noise as the ability of an i open quantum annealing system to yield the correct solution with hi = –1 acceptable probability within a time comparable to the closed- J = –1 system adiabatic timescale. ij 300 Experimental results. To experimentally investigate the effects of b noise on performance, we design an instance of HP that has an Δ i anticrossing with a small gmin between an eigenstate jiÆ , which is 200 (mK) a superposition of 256 equal energy (degenerate) local minima B k of HP, and an eigenstateji GM , which corresponds to the unique (nondegenerate) global minimum.
Load more

Recommended publications
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • 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.
    [Show full text]
  • Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis
    SANDIA REPORT SAND2015-3025 Unlimited Release June 2015 Benchmarking Adiabatic Quantum Optimization for Complex Network Analysis Ojas Parekh, Jeremy Wendt, Luke Shulenburger, Andrew Landahl, Jonathan Moussa, John Aidun Prepared by Sandia National Laboratories Albuquerque, New Mexico 87185 and Livermore, California 94550 Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. Approved for public release; further dissemination unlimited. 1 Issued by Sandia National Laboratories, operated for the United States Department of Energy by Sandia Corporation. NOTICE: This report was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government, nor any agency thereof, nor any of their employees, nor any of their contractors, subcontractors, or their employees, make any warranty, express or implied, or assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represent that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise, does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government, any agency thereof, or any of their contractors or subcontractors. The views and opinions expressed herein do not necessarily state or reflect those of the United States Government, any agency thereof, or any of their contractors. Printed in the United States of America.
    [Show full text]
  • Quantum Annealing: the Fastest Route to Quantum Computation?
    Eur. Phys. J. Special Topics 224, 1–4 (2015) © EDP Sciences, Springer-Verlag 2015 THE EUROPEAN DOI: 10.1140/epjst/e2015-02336-2 PHYSICAL JOURNAL SPECIAL TOPICS Editorial Quantum annealing: The fastest route to quantum computation? Sei Suzuki1,a and Arnab Das2,b 1 Department of Liberal Arts, Saitama Medical University 38 Moro-hongo, Moroyama, Saitama 350-0495, Japan 2 Theoretical Physics Department, Indian Association for the Cultivation of Science 2A & 2B Raja S. C. Mullick Road, Kolkata 700032, India Received 9 December 2014 / Received in final form 16 December 2014 Published online 5 February 2015 What is Quantum Computation. The world at the microscopic scale, i.e. at the scale of an atom or an electron, looks very different from the world of our daily life experiences. The very idea of the “state” of a microscopic (quantum) object is rad- ically different from that of a macroscopic (classical) object. According to our daily experiences, at any given instant a system (say a switch or a particle) remains in a definite state characterized by some definite set of values of its physical properties. For example, a switch will either be in the state “off” state or in the state “on” but never in a state of simultaneously being on and off. Similarly, a particle moving on a surface (say) must be located at some definite point R on the surface at a given instant, and can’t be simultaneously present at different points. But in the quantum world, a switch is allowed to be in a state which is a simultaneous “superposition” of both on and off states, and a particle can remain in a state that is “delocalized” over the entire surface at the same instant.
    [Show full text]
  • Complex Networks from Classical to Quantum
    REVIEW ARTICLE https://doi.org/10.1038/s42005-019-0152-6 OPEN Complex networks from classical to quantum Jacob Biamonte 1, Mauro Faccin2 & Manlio De Domenico3 Recent progress in applying complex network theory to problems in quantum information has 1234567890():,; resulted in a beneficial cross-over. Complex network methods have successfully been applied to transport and entanglement models while information physics is setting the stage for a theory of complex systems with quantum information-inspired methods. Novel quantum induced effects have been predicted in random graphs—where edges represent entangled links—and quantum computer algorithms have been proposed to offer enhancement for several network problems. Here we review the results at the cutting edge, pinpointing the similarities and the differences found at the intersection of these two fields. uantum mechanics has long been predicted to help solve computational problems in physics1, chemistry2, and machine learning3 and to offer quantum security enhancement Q 4 5 in communications , including a quantum secure Internet . Rapid experimental pro- gress has pushed quantum computing and communication devices into truly data-intensive domains, where even the classical network describing a quantum system can exhibit complex features, giving rise to what appears as a paradigm shift needed to face a fundamental type of complexity6–13. Methods originating in complex networks—traditionally based on statistical mechanics—are now being generalized to the quantum domain in order to address these new quantum complexity challenges. Building on several fundamental discoveries14,15, complex network theory has demonstrated that many (non-quantum) systems exhibit similarities in their complex features14–18, in the organization of their structure and dynamics19–24, the controllability of their constituents25, and their resilience to structural and dynamical perturbations26–31.
    [Show full text]
  • High Energy Physics Quantum Information Science Awards Abstracts
    High Energy Physics Quantum Information Science Awards Abstracts Towards Directional Detection of WIMP Dark Matter using Spectroscopy of Quantum Defects in Diamond Ronald Walsworth, David Phillips, and Alexander Sushkov Challenges and Opportunities in Noise‐Aware Implementations of Quantum Field Theories on Near‐Term Quantum Computing Hardware Raphael Pooser, Patrick Dreher, and Lex Kemper Quantum Sensors for Wide Band Axion Dark Matter Detection Peter S Barry, Andrew Sonnenschein, Clarence Chang, Jiansong Gao, Steve Kuhlmann, Noah Kurinsky, and Joel Ullom The Dark Matter Radio‐: A Quantum‐Enhanced Dark Matter Search Kent Irwin and Peter Graham Quantum Sensors for Light-field Dark Matter Searches Kent Irwin, Peter Graham, Alexander Sushkov, Dmitry Budke, and Derek Kimball The Geometry and Flow of Quantum Information: From Quantum Gravity to Quantum Technology Raphael Bousso1, Ehud Altman1, Ning Bao1, Patrick Hayden, Christopher Monroe, Yasunori Nomura1, Xiao‐Liang Qi, Monika Schleier‐Smith, Brian Swingle3, Norman Yao1, and Michael Zaletel Algebraic Approach Towards Quantum Information in Quantum Field Theory and Holography Daniel Harlow, Aram Harrow and Hong Liu Interplay of Quantum Information, Thermodynamics, and Gravity in the Early Universe Nishant Agarwal, Adolfo del Campo, Archana Kamal, and Sarah Shandera Quantum Computing for Neutrino‐nucleus Dynamics Joseph Carlson, Rajan Gupta, Andy C.N. Li, Gabriel Perdue, and Alessandro Roggero Quantum‐Enhanced Metrology with Trapped Ions for Fundamental Physics Salman Habib, Kaifeng Cui1,
    [Show full text]
  • Realisation of Qudits in Coupled Potential Wells Ariel Landau Tel Aviv University
    Chapman University Chapman University Digital Commons Mathematics, Physics, and Computer Science Science and Technology Faculty Articles and Faculty Articles and Research Research 8-23-2016 Realisation of Qudits in Coupled Potential Wells Ariel Landau Tel Aviv University Yakir Aharonov Chapman University, [email protected] Eliahu Cohen University of Bristol Follow this and additional works at: http://digitalcommons.chapman.edu/scs_articles Part of the Quantum Physics Commons Recommended Citation Landau, A., Aharonov, Y., Cohen, E., 2016. Realization of qudits in coupled potential wells. Int. J. Quantum Inform. 14, 1650029. doi:10.1142/S0219749916500295 This Article is brought to you for free and open access by the Science and Technology Faculty Articles and Research at Chapman University Digital Commons. It has been accepted for inclusion in Mathematics, Physics, and Computer Science Faculty Articles and Research by an authorized administrator of Chapman University Digital Commons. For more information, please contact [email protected]. Realisation of Qudits in Coupled Potential Wells Comments This is a pre-copy-editing, author-produced PDF of an article accepted for publication in International Journal of Quantum Information, volume 14, in 2016 following peer review. The definitive publisher-authenticated version is available online at DOI: 10.1142/S0219749916500295. Copyright World Scientific This article is available at Chapman University Digital Commons: http://digitalcommons.chapman.edu/scs_articles/383 Realisation of Qudits in Coupled Potential Wells Ariel Landau1, Yakir Aharonov1;2, Eliahu Cohen3 1School of Physics and Astronomy, Tel-Aviv University, Tel-Aviv 6997801, Israel 2Schmid College of Science, Chapman University, Orange, CA 92866, USA 3H.H. Wills Physics Laboratory, University of Bristol, Tyndall Avenue, Bristol, BS8 1TL, U.K PACS numbers: ABSTRACT to study the analogue 3-state register, the qutrit, and more generally the d-state qudit.
    [Show full text]
  • Machine Learning of a Higgs Decay Classifier Via Quantum Annealing
    Machine learning of a Higgs decay classifier via quantum annealing Presenter: Joshua Job1 Reference: “Solving a Higgs optimization problem with quantum annealing for machine learning”, forthcoming, Nature Collaborators: Alex Mott2, Jean-Roch Vlimant2, Daniel Lidar3, Maria Spiropulu2 Associations: 1. Department of Physics, Center for Quantum Information Science & Technology, University of Southern California 2. Department of Physics, California Institute of Technology 3. Departments of Electrical Engineering, Chemistry, and Physics, Center for Quantum Information Science & Technology, University of Southern California ● The problem: Higgs detection at the Large Hadron Collider ● Quantum annealing overview ● Our technique: Quantum annealing for Outline machine learning ● Results ● Future directions ● Acknowledgements The problem: Higgs detection at the Large Hadron Collider The LHC: ● Large Hadron Collider -- 27km ring ● Cost: ~$4.5 billion Basic challenge: ● LHC produces 600 million collisions/second, generating ~75TB/sec of data Basic challenge: ● LHC produces 600 million collisions/second, generating ~75TB/sec of data ● Like the Biblical flood Basic challenge: ● LHC produces 600 million collisions/second, generating ~75TB/sec of data ● Like the Biblical flood ● Cut down to something closer to Niagara Falls, 1GB/sec of data What process are we looking for anyway? A Higgs decaying into two photons, i.e the H⟶γγ process Background processes are, for instance, gg⟶γγ events How they do it: ● Nested sets of triggers selecting the most interesting
    [Show full text]