A Quantum Compiler for Topological Quantum Computation Caitlin Carnahan
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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. -
COVID-19 Detection on IBM Quantum Computer with Classical-Quantum Transfer Learning
medRxiv preprint doi: https://doi.org/10.1101/2020.11.07.20227306; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Turk J Elec Eng & Comp Sci () : { © TUB¨ ITAK_ doi:10.3906/elk- COVID-19 detection on IBM quantum computer with classical-quantum transfer learning Erdi ACAR1*, Ihsan_ YILMAZ2 1Department of Computer Engineering, Institute of Science, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey 2Department of Computer Engineering, Faculty of Engineering, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey Received: .201 Accepted/Published Online: .201 Final Version: ..201 Abstract: Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. -
BCG) Is a Global Management Consulting Firm and the World’S Leading Advisor on Business Strategy
The Next Decade in Quantum Computing— and How to Play Boston Consulting Group (BCG) is a global management consulting firm and the world’s leading advisor on business strategy. We partner with clients from the private, public, and not-for-profit sectors in all regions to identify their highest-value opportunities, address their most critical challenges, and transform their enterprises. Our customized approach combines deep insight into the dynamics of companies and markets with close collaboration at all levels of the client organization. This ensures that our clients achieve sustainable competitive advantage, build more capable organizations, and secure lasting results. Founded in 1963, BCG is a private company with offices in more than 90 cities in 50 countries. For more information, please visit bcg.com. THE NEXT DECADE IN QUANTUM COMPUTING— AND HOW TO PLAY PHILIPP GERBERT FRANK RUESS November 2018 | Boston Consulting Group CONTENTS 3 INTRODUCTION 4 HOW QUANTUM COMPUTERS ARE DIFFERENT, AND WHY IT MATTERS 6 THE EMERGING QUANTUM COMPUTING ECOSYSTEM Tech Companies Applications and Users 10 INVESTMENTS, PUBLICATIONS, AND INTELLECTUAL PROPERTY 13 A BRIEF TOUR OF QUANTUM COMPUTING TECHNOLOGIES Criteria for Assessment Current Technologies Other Promising Technologies Odd Man Out 18 SIMPLIFYING THE QUANTUM ALGORITHM ZOO 21 HOW TO PLAY THE NEXT FIVE YEARS AND BEYOND Determining Timing and Engagement The Current State of Play 24 A POTENTIAL QUANTUM WINTER, AND THE OPPORTUNITY THEREIN 25 FOR FURTHER READING 26 NOTE TO THE READER 2 | The Next Decade in Quantum Computing—and How to Play INTRODUCTION he experts are convinced that in time they can build a Thigh-performance quantum computer. -
A Functional Architecture for Scalable Quantum Computing
A Functional Architecture for Scalable Quantum Computing Eyob A. Sete, William J. Zeng, Chad T. Rigetti Rigetti Computing Berkeley, California Email: feyob,will,[email protected] Abstract—Quantum computing devices based on supercon- ducting quantum circuits have rapidly developed in the last few years. The building blocks—superconducting qubits, quantum- limited amplifiers, and two-qubit gates—have been demonstrated by several groups. Small prototype quantum processor systems have been implemented with performance adequate to demon- strate quantum chemistry simulations, optimization algorithms, and enable experimental tests of quantum error correction schemes. A major bottleneck in the effort to devleop larger systems is the need for a scalable functional architecture that combines all thee core building blocks in a single, scalable technology. We describe such a functional architecture, based on Fig. 1. A schematic of a functional layout of a quantum computing system. a planar lattice of transmon and fluxonium qubits, parametric amplifiers, and a novel fast DC controlled two-qubit gate. kBT should be much less than the energy of the photon at the Keywords—quantum computing, superconducting integrated qubit transition energy ~!01. In a large system where supercon- circuits, computer architecture. ducting circuits are packed together, unwanted crosstalk among devices is inevitable. To reduce or minimize crosstalk, efficient I. INTRODUCTION packaging and isolation of individual devices is imperative and substantially improves the performance of the system. The underlying hardware for quantum computing has ad- vanced to make the design of the first scalable quantum Superconducting qubits are made using Josephson tunnel computers possible. Superconducting chips with 4–9 qubits junctions which are formed by two superconducting thin have been demonstrated with the performance required to electrodes separated by a dielectric material. -
Experimental Kernel-Based Quantum Machine Learning in Finite Feature
www.nature.com/scientificreports OPEN Experimental kernel‑based quantum machine learning in fnite feature space Karol Bartkiewicz1,2*, Clemens Gneiting3, Antonín Černoch2*, Kateřina Jiráková2, Karel Lemr2* & Franco Nori3,4 We implement an all‑optical setup demonstrating kernel‑based quantum machine learning for two‑ dimensional classifcation problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer. Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space. In order to maximize the application range of the deployable kernels, we optimize feature maps towards the resulting kernels’ ability to separate points, i.e., their “resolution,” under the constraint of fnite, fxed Hilbert space dimension. Implementing these kernels, our setup delivers viable decision boundaries for standard nonlinear supervised classifcation tasks in feature space. We demonstrate such kernel-based quantum machine learning using specialized multiphoton quantum optical circuits. The deployed kernel exhibits exponentially better scaling in the required number of qubits than a direct generalization of kernels described in the literature. Many contemporary computational problems (like drug design, trafc control, logistics, automatic driving, stock market analysis, automatic medical examination, material engineering, and others) routinely require optimiza- tion over huge amounts of data1. While these highly demanding problems can ofen be approached by suitable machine learning (ML) algorithms, in many relevant cases the underlying calculations would last prohibitively long. Quantum ML (QML) comes with the promise to run these computations more efciently (in some cases exponentially faster) by complementing ML algorithms with quantum resources. -
High Energy Physics Quantum Computing
High Energy Physics Quantum Computing Quantum Information Science in High Energy Physics at the Large Hadron Collider PI: O.K. Baker, Yale University Unraveling the quantum structure of QCD in parton shower Monte Carlo generators PI: Christian Bauer, Lawrence Berkeley National Laboratory Co-PIs: Wibe de Jong and Ben Nachman (LBNL) The HEP.QPR Project: Quantum Pattern Recognition for Charged Particle Tracking PI: Heather Gray, Lawrence Berkeley National Laboratory Co-PIs: Wahid Bhimji, Paolo Calafiura, Steve Farrell, Wim Lavrijsen, Lucy Linder, Illya Shapoval (LBNL) Neutrino-Nucleus Scattering on a Quantum Computer PI: Rajan Gupta, Los Alamos National Laboratory Co-PIs: Joseph Carlson (LANL); Alessandro Roggero (UW), Gabriel Purdue (FNAL) Particle Track Pattern Recognition via Content-Addressable Memory and Adiabatic Quantum Optimization PI: Lauren Ice, Johns Hopkins University Co-PIs: Gregory Quiroz (Johns Hopkins); Travis Humble (Oak Ridge National Laboratory) Towards practical quantum simulation for High Energy Physics PI: Peter Love, Tufts University Co-PIs: Gary Goldstein, Hugo Beauchemin (Tufts) High Energy Physics (HEP) ML and Optimization Go Quantum PI: Gabriel Perdue, Fermilab Co-PIs: Jim Kowalkowski, Stephen Mrenna, Brian Nord, Aris Tsaris (Fermilab); Travis Humble, Alex McCaskey (Oak Ridge National Lab) Quantum Machine Learning and Quantum Computation Frameworks for HEP (QMLQCF) PI: M. Spiropulu, California Institute of Technology Co-PIs: Panagiotis Spentzouris (Fermilab), Daniel Lidar (USC), Seth Lloyd (MIT) Quantum Algorithms for Collider Physics PI: Jesse Thaler, Massachusetts Institute of Technology Co-PI: Aram Harrow, Massachusetts Institute of Technology Quantum Machine Learning for Lattice QCD PI: Boram Yoon, Los Alamos National Laboratory Co-PIs: Nga T. T. Nguyen, Garrett Kenyon, Tanmoy Bhattacharya and Rajan Gupta (LANL) Quantum Information Science in High Energy Physics at the Large Hadron Collider O.K. -
Chapter 3 Quantum Circuit Model of Computation
Chapter 3 Quantum Circuit Model of Computation 3.1 Introduction In the previous chapter we saw that any Boolean function can be computed from a reversible circuit. In particular, in principle at least, this can be done from a small set of universal logical gates which do not dissipate heat dissi- pation (so we have logical reversibility as well as physical reversibility. We also saw that quantum evolution of the states of a system (e.g. a collection of quantum bits) is unitary (ignoring the reading or measurement process of the bits). A unitary matrix is of course invertible, but also conserves entropy (at the present level you can think of this as a conservation of scalar prod- uct which implies conservation of probabilities and entropies). So quantum evolution is also both \logically" and physically reversible. The next natural question is then to ask if we can use quantum operations to perform computational tasks, such as computing a Boolean function? Do the principles of quantum mechanics bring in new limitations with respect to classical computations? Or do they on the contrary bring in new ressources and advantages? These issues were raised and discussed by Feynman, Benioff and Manin in the early 1980's. In principle QM does not bring any new limitations, but on the contrary the superposition principle applied to many particle systems (many qubits) enables us to perform parallel computations, thereby speed- ing up classical computations. This was recognized very early by Feynman who argued that classical computers cannot simulate efficiently quantum me- chanical processes. The basic reason is that general quantum states involve 1 2 CHAPTER 3. -
Luigi Frunzio
THESE DE DOCTORAT DE L’UNIVERSITE PARIS-SUD XI spécialité: Physique des solides présentée par: Luigi Frunzio pour obtenir le grade de DOCTEUR de l’UNIVERSITE PARIS-SUD XI Sujet de la thèse: Conception et fabrication de circuits supraconducteurs pour l'amplification et le traitement de signaux quantiques soutenue le 18 mai 2006, devant le jury composé de MM.: Michel Devoret Daniel Esteve, President Marc Gabay Robert Schoelkopf Rapporteurs scientifiques MM.: Antonio Barone Carlo Cosmelli ii Table of content List of Figures vii List of Symbols ix Acknowledgements xvii 1. Outline of this work 1 2. Motivation: two breakthroughs of quantum physics 5 2.1. Quantum computation is possible 5 2.1.1. Classical information 6 2.1.2. Quantum information unit: the qubit 7 2.1.3. Two new properties of qubits 8 2.1.4. Unique property of quantum information 10 2.1.5. Quantum algorithms 10 2.1.6. Quantum gates 11 2.1.7. Basic requirements for a quantum computer 12 2.1.8. Qubit decoherence 15 2.1.9. Quantum error correction codes 16 2.2. Macroscopic quantum mechanics: a quantum theory of electrical circuits 17 2.2.1. A natural test bed: superconducting electronics 18 2.2.2. Operational criteria for quantum circuits 19 iii 2.2.3. Quantum harmonic LC oscillator 19 2.2.4. Limits of circuits with lumped elements: need for transmission line resonators 22 2.2.5. Hamiltonian of a classical electrical circuit 24 2.2.6. Quantum description of an electric circuit 27 2.2.7. Caldeira-Leggett model for dissipative elements 27 2.2.8. -
Optimization of Reversible Circuits Using Toffoli Decompositions with Negative Controls
S S symmetry Article Optimization of Reversible Circuits Using Toffoli Decompositions with Negative Controls Mariam Gado 1,2,* and Ahmed Younes 1,2,3 1 Department of Mathematics and Computer Science, Faculty of Science, Alexandria University, Alexandria 21568, Egypt; [email protected] 2 Academy of Scientific Research and Technology(ASRT), Cairo 11516, Egypt 3 School of Computer Science, University of Birmingham, Birmingham B15 2TT, UK * Correspondence: [email protected]; Tel.: +203-39-21595; Fax: +203-39-11794 Abstract: The synthesis and optimization of quantum circuits are essential for the construction of quantum computers. This paper proposes two methods to reduce the quantum cost of 3-bit reversible circuits. The first method utilizes basic building blocks of gate pairs using different Toffoli decompositions. These gate pairs are used to reconstruct the quantum circuits where further optimization rules will be applied to synthesize the optimized circuit. The second method suggests using a new universal library, which provides better quantum cost when compared with previous work in both cost015 and cost115 metrics; this proposed new universal library “Negative NCT” uses gates that operate on the target qubit only when the control qubit’s state is zero. A combination of the proposed basic building blocks of pairs of gates and the proposed Negative NCT library is used in this work for synthesis and optimization, where the Negative NCT library showed better quantum cost after optimization compared with the NCT library despite having the same circuit size. The reversible circuits over three bits form a permutation group of size 40,320 (23!), which Citation: Gado, M.; Younes, A. -
Multi-Mode Ultra-Strong Coupling in Circuit Quantum Electrodynamics
www.nature.com/npjqi ARTICLE OPEN Multi-mode ultra-strong coupling in circuit quantum electrodynamics Sal J. Bosman1, Mario F. Gely1, Vibhor Singh2, Alessandro Bruno3, Daniel Bothner1 and Gary A. Steele1 With the introduction of superconducting circuits into the field of quantum optics, many experimental demonstrations of the quantum physics of an artificial atom coupled to a single-mode light field have been realized. Engineering such quantum systems offers the opportunity to explore extreme regimes of light-matter interaction that are inaccessible with natural systems. For instance the coupling strength g can be increased until it is comparable with the atomic or mode frequency ωa,m and the atom can be coupled to multiple modes which has always challenged our understanding of light-matter interaction. Here, we experimentally realize a transmon qubit in the ultra-strong coupling regime, reaching coupling ratios of g/ωm = 0.19 and we measure multi-mode interactions through a hybridization of the qubit up to the fifth mode of the resonator. This is enabled by a qubit with 88% of its capacitance formed by a vacuum-gap capacitance with the center conductor of a coplanar waveguide resonator. In addition to potential applications in quantum information technologies due to its small size, this architecture offers the potential to further explore the regime of multi-mode ultra-strong coupling. npj Quantum Information (2017) 3:46 ; doi:10.1038/s41534-017-0046-y INTRODUCTION decreasing gate times20 as well as the performance of quantum 21 Superconducting circuits such as microwave cavities and Joseph- memories. With very strong coupling rates, the additional modes son junction based artificial atoms1 have opened up a wealth of of an electromagnetic resonator become increasingly relevant, new experimental possibilities by enabling much stronger light- and U/DSC can only be understood in these systems if the multi- matter coupling than in analog experiments with natural atoms2 mode effects are correctly modeled. -
Concentric Transmon Qubit Featuring Fast Tunability and an Anisotropic Magnetic Dipole Moment
Concentric transmon qubit featuring fast tunability and an anisotropic magnetic dipole moment Cite as: Appl. Phys. Lett. 108, 032601 (2016); https://doi.org/10.1063/1.4940230 Submitted: 13 October 2015 . Accepted: 07 January 2016 . Published Online: 21 January 2016 Jochen Braumüller, Martin Sandberg, Michael R. Vissers, Andre Schneider, Steffen Schlör, Lukas Grünhaupt, Hannes Rotzinger, Michael Marthaler, Alexander Lukashenko, Amadeus Dieter, Alexey V. Ustinov, Martin Weides, and David P. Pappas ARTICLES YOU MAY BE INTERESTED IN A quantum engineer's guide to superconducting qubits Applied Physics Reviews 6, 021318 (2019); https://doi.org/10.1063/1.5089550 Planar superconducting resonators with internal quality factors above one million Applied Physics Letters 100, 113510 (2012); https://doi.org/10.1063/1.3693409 An argon ion beam milling process for native AlOx layers enabling coherent superconducting contacts Applied Physics Letters 111, 072601 (2017); https://doi.org/10.1063/1.4990491 Appl. Phys. Lett. 108, 032601 (2016); https://doi.org/10.1063/1.4940230 108, 032601 © 2016 AIP Publishing LLC. APPLIED PHYSICS LETTERS 108, 032601 (2016) Concentric transmon qubit featuring fast tunability and an anisotropic magnetic dipole moment Jochen Braumuller,€ 1,a) Martin Sandberg,2 Michael R. Vissers,2 Andre Schneider,1 Steffen Schlor,€ 1 Lukas Grunhaupt,€ 1 Hannes Rotzinger,1 Michael Marthaler,3 Alexander Lukashenko,1 Amadeus Dieter,1 Alexey V. Ustinov,1,4 Martin Weides,1,5 and David P. Pappas2 1Physikalisches Institut, Karlsruhe Institute of Technology,