DOCSLIB.ORG

Qdiff: Differential Testing of Quantum Software Stacks

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Qdiff: Differential Testing of Quantum Software Stacks QDiff: Differential Testing of Quantum Software Stacks Jiyuan Wang, Qian Zhang, Guoqing Harry Xu, Miryung Kim University of California, Los Angeles CA, United States fwangjiyuan, zhangqian, harryxu, [email protected] Abstract—Over the past few years, several quantum software A quantum software track (QSS) includes (1) APIs and stacks (QSS) have been developed in response to rapid hardware language constructs to express quantum algorithms, (2) a advances in quantum computing. A QSS includes a quantum compiler that transforms and optimizes a given input quantum programming language, an optimizing compiler that translates a quantum algorithm written in a high-level language into quantum algorithm at the circuit level, and (3) a backend executor that gate instructions, a quantum simulator that emulates these either simulates the resulting gates on classical devices or instructions on a classical device, and a software controller that executes directly on quantum hardware. Consider Qiskit [4], sends analog signals to a very expensive quantum hardware based which consists of four components: QiskitTerra that com- on quantum circuits. In comparison to traditional compilers piles and optimizes programs, QiskitAer that supports high- and architecture simulators, QSSes are difficult to tests due to QiskitIgnis the probabilistic nature of results, the lack of clear hardware performance noisy simulations, for error cor- specifications, and quantum programming complexity. rection, noise characterization, and hardware verification, and This work devises a novel differential testing approach for QiskitAqua that helps a developer to express a quantum QSSes, named QDIFF with three major innovations: (1) We algorithm or an application. Other QSSes [2] such as Pyquil generate input programs to be tested via semantics-preserving, have a similar set of components—e.g., Pyquil uses quilc as source to source transformation to explore program variants. qvm (2) We speed up differential testing by filtering out quantum a compiler and as a quantum simulator. circuits that are not worthwhile to execute on quantum hardware As with any compiler framework, a QSS could be error- by analyzing static characteristics such as a circuit depth, 2- prone. Developers and users often report bugs on popular gate operations, gate error rates, and T1 relaxation time. (3) QSSes [3], [5], [6], and a simple search on StackOverflow We design an extensible equivalence checking mechanism via distribution comparison functions such as Kolmogorov–Smirnov with the keyword “quantum error” would bring up over 500 test and cross entropy. posts on various QSS components, ranging from compiler We evaluate QDIFF with three widely-used open source QSSes: settings, simulation, and the actual hardware [7]. These posts Qiskit from IBM, Cirq from Google, and Pyquil from Rigetti. By often reveal deeper confusion that developers face due to running QDIFF on both real hardware and quantum simulators, inherent probabilistic nature of quantum measurements—if a we found several critical bugs revealing potential instabilities program produces a result that looks different from what is in these platforms. QDIFF’s source transformation is effective in producing semantically equivalent yet not-identical circuits expected, is it due to a bug or the non-determinism inherent (i.e., 34% of trials), and its filtering mechanism can speed up in quantum programs? Is there divergence beyond expected differential testing by 66%. noise coming from an input program, a compiler, a simulator, and/or hardware? I. INTRODUCTION Quantum computing is an emerging computing paradigm Challenges. There are three technical challenges that make it that promises unique advantages over classical computing. difficult to test QSSes. However, quantum programming is challenging. Quantum The first challenge is how to generate semantically equiva- computation logic is expressed in qubits and quantum gates, lent programs for testing quantum compilers [8], [9]. Though and the states of quantum registers are measured probabilis- existing compiler testing techniques generate equivalent pro- tically. Due to the physical properties of quantum mechanics, grams by manipulating code in dead regions or unexecuted qubits and quantum gates are fundamentally different from branches [10], a quantum program usually does not have many classical bits and gate logic. For example, quantum indeter- unexecuted branches. Therefore, we must design a technique minacy dictates that the same quantum program can produce that can produce a large number of semantically-equivalent different results in different executions. variant circuits that may induce divergence on hardware. To this end, many quantum software stacks such as Google’s The second challenge is that compilers are not the only Cirq [1], Rigetti’s Pyquil [2], and IBM’s Qiskit [3] have source of bugs in a QSS. For example, in a line of recent been developed to provide user-friendly high-level languages work [11], [12] on verified quantum compilers, compilers are for quantum programming, abstracting away the underlying guaranteed to be correct with respect to their optimization physical and mathematical intricacies. and transformation rules. However, the overall correctness of QSSes goes beyond compiler optimization rules. We must of logically equivalent circuits such as the circuit depth (i.e., reason about subsequent execution on quantum simulators and moments), the number of 2-qubit gates, the known error hardware, which are often the major sources of instabilities. rates of each gate kind, and T1 relaxation time for quantum In fact, Rigetti or Cirq’s Github histories indicate that bugs hardware. QDIFF obviates the need of executing certain circuits may come from its various backends due to improper initial- on hardware or noisy simulators, if the logically equivalent ization [13], problematic APIs [14], or inappropriate synthesis circuits are incapable of revealing meaningful instabilities in options [15]. QSS. The third challenge is how to interpret measurements from Third, in order to compare executions of logically equivalent quantum simulators or hardware, since quantum measurement circuits, QDIFF first identifies how many measurements are operations by definition are probabilistic in nature due to needed for reliable comparison. Based on the literature of quantum indeterminacy [11]. Therefore, when we execute distribution sampling, QDIFF adapts closeness testing in L1 logically equivalent program variants, comparing their mea- norm [17] to estimate the required number of measurements surement results is not straightforward, as we must take into given confidence level p = 2=3. Using either a noisy simulator account inherent noise (e.g., hardware gate errors, readout or directly on hardware, it then performs the required number errors, and decoherence errors) and must determine whether of measurements. To compare two sets of measurements, the observed divergence is significant enough to be considered QDIFF uses distribution comparison methods. Two methods as a meaningful instability. based on Kolmogorov-Smirnov (K-S) test [18], [19] and Cross Entropy [20], [21] are currently supported. This com- This Work. QDIFF is a novel differential testing technique for parison method is easily extensible in QDIFF, as a user can QSSes. QDIFF takes as input a seed quantum program and define a new statistical comparison method [17] in QDIFF. reports potential bugs with a pair of witness programs (i.e., logically equivalent programs that are supposed to produce Results. We evaluated QDIFF with the latest versions of three identical behavior) but triggers divergent behavior on the target widely-used QSSes: Qiskit, Cirq, and Pyquil. With six seed QSS (i.e., meaningful instabilities beyond expected noise). quantum algorithms, QDIFF generates 730 variant algorithms QDIFF overcomes the aforementioned challenges with three through semantics-modifying mutations. Starting from the technical innovations below. generated algorithms, it generates a total of 14799 program variants using semantics-preserving source transformations. First, QDIFF generates logically equivalent quantum pro- This generation process took QDIFF 14 hours. With the filtering grams using a set of equivalent gate transformation rules. mechanism, QDIFF reduces its testing time by 66%. Using This is based on the insight that each quantum gate can be QDIFF, we determined total 6 sources of instabilities. These represented mathematically as a unitary matrix; a sequence include 4 software crash bugs in Pyquil and Cirq simulation, of gates essentially corresponds to the multiplication of their and 2 potential root causes that may explain 25 out of 29 cases unitary matrices, which also yields a unitary matrix. As such, of divergence beyond expected noise on IBM hardware. one sequence of gates is semantically equivalent to another if the two sequences yield the same unitary matrix. We The main contributions of this work include: leverage seven gate transformation rules to generate different • We present the first differential testing framework for gate sequences that are guaranteed to yield the same unitary QSSes. This framework is integrated with three widely matrix. These sequences essentially define logically equivalent used QSSes from Google, IBM, and Rigetti. program variants that are supposed to yield identical behavior. • We embody three technical innovations in QDIFF to make Such variants are then executed on a simulator or hardware differential testing
Load more

Recommended publications
  • Qiskit Backend Specifications for Openqasm and Openpulse
    Qiskit Backend Specifications for OpenQASM and OpenPulse Experiments David C. McKay1,∗ Thomas Alexander1, Luciano Bello1, Michael J. Biercuk2, Lev Bishop1, Jiayin Chen2, Jerry M. Chow1, Antonio D. C´orcoles1, Daniel Egger1, Stefan Filipp1, Juan Gomez1, Michael Hush2, Ali Javadi-Abhari1, Diego Moreda1, Paul Nation1, Brent Paulovicks1, Erick Winston1, Christopher J. Wood1, James Wootton1 and Jay M. Gambetta1 1IBM Research 2Q-CTRL Pty Ltd, Sydney NSW Australia September 11, 2018 Abstract As interest in quantum computing grows, there is a pressing need for standardized API's so that algorithm designers, circuit designers, and physicists can be provided a common reference frame for designing, executing, and optimizing experiments. There is also a need for a language specification that goes beyond gates and allows users to specify the time dynamics of a quantum experiment and recover the time dynamics of the output. In this document we provide a specification for a common interface to backends (simulators and experiments) and a standarized data structure (Qobj | quantum object) for sending experiments to those backends via Qiskit. We also introduce OpenPulse, a language for specifying pulse level control (i.e. control of the continuous time dynamics) of a general quantum device independent of the specific arXiv:1809.03452v1 [quant-ph] 10 Sep 2018 hardware implementation. ∗[email protected] 1 Contents 1 Introduction3 1.1 Intended Audience . .4 1.2 Outline of Document . .4 1.3 Outside of the Scope . .5 1.4 Interface Language and Schemas . .5 2 Qiskit API5 2.1 General Overview of a Qiskit Experiment . .7 2.2 Provider . .8 2.3 Backend .
    [Show full text]
  • Qubit Coupled Cluster Singles and Doubles Variational Quantum Eigensolver Ansatz for Electronic Structure Calculations
    Quantum Science and Technology PAPER Qubit coupled cluster singles and doubles variational quantum eigensolver ansatz for electronic structure calculations To cite this article: Rongxin Xia and Sabre Kais 2020 Quantum Sci. Technol. 6 015001 View the article online for updates and enhancements. This content was downloaded from IP address 128.210.107.25 on 20/10/2020 at 12:02 Quantum Sci. Technol. 6 (2021) 015001 https://doi.org/10.1088/2058-9565/abbc74 PAPER Qubit coupled cluster singles and doubles variational quantum RECEIVED 27 July 2020 eigensolver ansatz for electronic structure calculations REVISED 14 September 2020 Rongxin Xia and Sabre Kais∗ ACCEPTED FOR PUBLICATION Department of Chemistry, Department of Physics and Astronomy, and Purdue Quantum Science and Engineering Institute, Purdue 29 September 2020 University, West Lafayette, United States of America ∗ PUBLISHED Author to whom any correspondence should be addressed. 20 October 2020 E-mail: [email protected] Keywords: variational quantum eigensolver, electronic structure calculations, unitary coupled cluster singles and doubles excitations Abstract Variational quantum eigensolver (VQE) for electronic structure calculations is believed to be one major potential application of near term quantum computing. Among all proposed VQE algorithms, the unitary coupled cluster singles and doubles excitations (UCCSD) VQE ansatz has achieved high accuracy and received a lot of research interest. However, the UCCSD VQE based on fermionic excitations needs extra terms for the parity when using Jordan–Wigner transformation. Here we introduce a new VQE ansatz based on the particle preserving exchange gate to achieve qubit excitations. The proposed VQE ansatz has gate complexity up-bounded to O(n4)for all-to-all connectivity where n is the number of qubits of the Hamiltonian.
    [Show full text]
  • Arxiv:1812.09167V1 [Quant-Ph] 21 Dec 2018 It with the Tex Typesetting System Being a Prime Example
    Open source software in quantum computing Mark Fingerhutha,1, 2 Tomáš Babej,1 and Peter Wittek3, 4, 5, 6 1ProteinQure Inc., Toronto, Canada 2University of KwaZulu-Natal, Durban, South Africa 3Rotman School of Management, University of Toronto, Toronto, Canada 4Creative Destruction Lab, Toronto, Canada 5Vector Institute for Artificial Intelligence, Toronto, Canada 6Perimeter Institute for Theoretical Physics, Waterloo, Canada Open source software is becoming crucial in the design and testing of quantum algorithms. Many of the tools are backed by major commercial vendors with the goal to make it easier to develop quantum software: this mirrors how well-funded open machine learning frameworks enabled the development of complex models and their execution on equally complex hardware. We review a wide range of open source software for quantum computing, covering all stages of the quantum toolchain from quantum hardware interfaces through quantum compilers to implementations of quantum algorithms, as well as all quantum computing paradigms, including quantum annealing, and discrete and continuous-variable gate-model quantum computing. The evaluation of each project covers characteristics such as documentation, licence, the choice of programming language, compliance with norms of software engineering, and the culture of the project. We find that while the diversity of projects is mesmerizing, only a few attract external developers and even many commercially backed frameworks have shortcomings in software engineering. Based on these observations, we highlight the best practices that could foster a more active community around quantum computing software that welcomes newcomers to the field, but also ensures high-quality, well-documented code. INTRODUCTION Source code has been developed and shared among enthusiasts since the early 1950s.
    [Show full text]
  • Building Your Quantum Capability
    Building your quantum capability The case for joining an “ecosystem” IBM Institute for Business Value 2 Building your quantum capability A quantum collaboration Quantum computing has the potential to solve difficult business problems that classical computers cannot. Within five years, analysts estimate that 20 percent of organizations will be budgeting for quantum computing projects and, within a decade, quantum computing may be a USD15 billion industry.1,2 To place your organization in the vanguard of change, you may need to consider joining a quantum computing ecosystem now. The reality is that quantum computing ecosystems are already taking shape, with members focused on how to solve targeted scientific and business problems. Joining the right quantum ecosystem today could give your organization a competitive advantage tomorrow. Building your quantum capability 3 The quantum landscape Quantum computing is gathering momentum. By joining a burgeoning quantum computing Today, major tech companies are backing ecosystem, your organization can get a head What is quantum computing? sizeable R&D programs, venture capital funding start today. Unlike “classical” computers, has grown to at least $300 million, dozens of quantum computers can represent a superposition of multiple states at startups have been founded, quantum A quantum ecosystem brings together business the same time. This characteristic is conferences and research papers are climbing, experts with specialists from industry, expected to provide more efficient research, academia, engineering, physics and students from 1,500 schools have used quantum solutions for certain complex business software development/coding across the computing online, and governments, including and scientific problems, such as drug China and the European Union, have announced quantum stack to develop quantum solutions and materials development, logistics billion-dollar investment programs.3,4,5 that solve business and science problems that and artificial intelligence.
    [Show full text]
  • Student User Experience with the IBM Qiskit Quantum Computing Interface
    Proceedings of Student-Faculty Research Day, CSIS, Pace University, May 4th, 2018 Student User Experience with the IBM QISKit Quantum Computing Interface Stephan Barabasi, James Barrera, Prashant Bhalani, Preeti Dalvi, Ryan Kimiecik, Avery Leider, John Mondrosch, Karl Peterson, Nimish Sawant, and Charles C. Tappert Seidenberg School of CSIS, Pace University, Pleasantville, New York Abstract - The field of quantum computing is rapidly Schrödinger [11]. Quantum mechanics states that the position expanding. As manufacturers and researchers grapple with the of a particle cannot be predicted with precision as it can be in limitations of classical silicon central processing units (CPUs), Newtonian mechanics. The only thing an observer could quantum computing sheds these limitations and promises a know about the position of a particle is the probability that it boom in computational power and efficiency. The quantum age will be at a certain position at a given time [11]. By the 1980’s will require many skilled engineers, mathematicians, physicists, developers, and technicians with an understanding of quantum several researchers including Feynman, Yuri Manin, and Paul principles and theory. There is currently a shortage of Benioff had begun researching computers that operate using professionals with a deep knowledge of computing and physics this concept. able to meet the demands of companies developing and The quantum bit or qubit is the basic unit of quantum researching quantum technology. This study provides a brief information. Classical computers operate on bits using history of quantum computing, an in-depth review of recent complex configurations of simple logic gates. A bit of literature and technologies, an overview of IBM’s QISKit for information has two states, on or off, and is represented with implementing quantum computing programs, and two a 0 or a 1.
    [Show full text]
  • Resource-Efficient Quantum Computing By
    Resource-Efficient Quantum Computing by Breaking Abstractions Fred Chong Seymour Goodman Professor Department of Computer Science University of Chicago Lead PI, the EPiQC Project, an NSF Expedition in Computing NSF 1730449/1730082/1729369/1832377/1730088 NSF Phy-1818914/OMA-2016136 DOE DE-SC0020289/0020331/QNEXT With Ken Brown, Ike Chuang, Diana Franklin, Danielle Harlow, Aram Harrow, Andrew Houck, John Reppy, David Schuster, Peter Shor Why Quantum Computing? n Fundamentally change what is computable q The only means to potentially scale computation exponentially with the number of devices n Solve currently intractable problems in chemistry, simulation, and optimization q Could lead to new nanoscale materials, better photovoltaics, better nitrogen fixation, and more n A new industry and scaling curve to accelerate key applications q Not a full replacement for Moore’s Law, but perhaps helps in key domains n Lead to more insights in classical computing q Previous insights in chemistry, physics and cryptography q Challenge classical algorithms to compete w/ quantum algorithms 2 NISQ Now is a privileged time in the history of science and technology, as we are witnessing the opening of the NISQ era (where NISQ = noisy intermediate-scale quantum). – John Preskill, Caltech IBM IonQ Google 53 superconductor qubits 79 atomic ion qubits 53 supercond qubits (11 controllable) Quantum computing is at the cusp of a revolution Every qubit doubles computational power Exponential growth in qubits … led to quantum supremacy with 53 qubits vs. Seconds Days Double exponential growth! 4 The Gap between Algorithms and Hardware The Gap between Algorithms and Hardware The Gap between Algorithms and Hardware The EPiQC Goal Develop algorithms, software, and hardware in concert to close the gap between algorithms and devices by 100-1000X, accelerating QC by 10-20 years.
    [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]
  • Quantum Computing with the IBM Quantum Experience with the Quantum Information Software Toolkit (Qiskit) Nick Bronn Research Staff Member IBM T.J
    Quantum Computing with the IBM Quantum Experience with the Quantum Information Software Toolkit (QISKit) Nick Bronn Research Staff Member IBM T.J. Watson Research Center ACM Poughkeepsie Monthly Meeting, January 2018 1 ©2017 IBM Corporation 25 January 2018 Overview Part 1: Quantum Computing § What, why, how § Quantum gates and circuits Part 2: Superconducting Qubits § Device properties § Control and performance Part 3: IBM Quantum Experience § Website: GUI, user guides, community § QISKit: API, SDK, Tutorials 2 ©2017 IBM Corporation 25 January 2018 Quantum computing: what, why, how 3 ©2017 IBM Corporation 25 January 2018 “Nature isn’t classical . if you want to make a simula6on of nature, you’d be:er make it quantum mechanical, and by golly it’s a wonderful problem, because it doesn’t look so easy.” – Richard Feynman, 1981 1st Conference on Physics and Computation, MIT 4 ©2017 IBM Corporation 25 January 2018 Computing with Quantum Mechanics: Features Superposion: a system’s state can be any linear combinaon of classical states …un#l it is measured, at which point it collapses to one of the classical states Example: Schrodinger’s Cat “Classical” states Quantum Normalizaon wavefuncon Entanglement: par0cles in superposi0on 1 ⎛ ⎞ ψ = ⎜ + ⎟ can develop correlaons such that 2 ⎜ ⎟ measuring just one affects them all ⎝ ⎠ Example: EPR Paradox (Einstein: “spooky Linear combinaon ac0on at a distance”) 5 ©2017 IBM Corporation 25 January 2018 Computing with Quantum Mechanics: Drawbacks 1 Decoherence: a system is gradually measured by residual interac0on with its environment, killing quantum behavior Qubit State Consequence: quantum effects observed only 0 Time in well-isolated systems (so not cats… yet) Uncertainty principle: measuring one variable (e.g.
    [Show full text]
  • Quantum Circuits for Maximally Entangled States
    Quantum circuits for maximally entangled states Alba Cervera-Lierta,1, 2, ∗ José Ignacio Latorre,2, 3, 4 and Dardo Goyeneche5 1Barcelona Supercomputing Center (BSC). 2Dept. Física Quàntica i Astrofísica, Universitat de Barcelona, Barcelona, Spain. 3Nikhef Theory Group, Science Park 105, 1098 XG Amsterdam, The Netherlands. 4Center for Quantum Technologies, National University of Singapore, Singapore. 5Dept. Física, Facultad de Ciencias Básicas, Universidad de Antofagasta, Casilla 170, Antofagasta, Chile. (Dated: May 16, 2019) We design a series of quantum circuits that generate absolute maximally entangled (AME) states to benchmark a quantum computer. A relation between graph states and AME states can be exploited to optimize the structure of the circuits and minimize their depth. Furthermore, we find that most of the provided circuits obey majorization relations for every partition of the system and every step of the algorithm. The main goal of the work consists in testing efficiency of quantum computers when requiring the maximal amount of genuine multipartite entanglement allowed by quantum mechanics, which can be used to efficiently implement multipartite quantum protocols. I. INTRODUCTION nique, e.g. tensor networks [9], is considered. We believe that item (i) is fully doable with the current state of the There is a need to set up a thorough benchmarking art of quantum computers, at least for a small number strategy for quantum computers. Devices that operate of qubits. On the other hand, item (ii) is much more in very different platforms are often characterized by the challenging, as classical computers efficiently work with number of qubits they offer, their coherent time and the a large number of bits.
    [Show full text]
  • Efficient Two-Electron Ansatz for Benchmarking Quantum Chemistry on a Quantum Computer
    PHYSICAL REVIEW RESEARCH 2, 023048 (2020) Efficient two-electron ansatz for benchmarking quantum chemistry on a quantum computer Scott E. Smart and David A. Mazziotti* Department of Chemistry and James Franck Institute, The University of Chicago, Chicago, Illinois 60637, USA (Received 18 December 2019; accepted 16 March 2020; published 17 April 2020) Quantum chemistry provides key applications for near-term quantum computing, but these are greatly complicated by the presence of noise. In this work we present an efficient ansatz for the computation of two- electron atoms and molecules within a hybrid quantum-classical algorithm. The ansatz exploits the fundamental structure of the two-electron system, treating the nonlocal and local degrees of freedom on the quantum and classical computers, respectively. Here the nonlocal degrees of freedom scale linearly with respect to basis-set size, giving a linear ansatz with only O(1) circuit preparations required for reduced state tomography. We implement this benchmark with error mitigation on two publicly available quantum computers, calculating + accurate dissociation curves for four- and six-qubit calculations of H2 and H3 . DOI: 10.1103/PhysRevResearch.2.023048 I. INTRODUCTION separation between the nonlocal and local fermionic degrees of freedom in the system [17], scaling linearly and polynomi- Quantum computers possess a natural affinity for quantum ally, respectively, and can be leveraged in a variational hybrid simulation and can transform exponentially scaling problems quantum-classical algorithm. The entangled nonlocal degrees into polynomial ones [1–3]. Quantum supremacy, the ability are treated on the quantum computer while the local degrees of a quantum computer to surpass its classical counterpart are treated on the classical computer, leading to an efficient on a designated task with lower asymptotic scaling, is poten- simulation of the system.
    [Show full text]
  • Supercomputer Simulations of Transmon Quantum Computers Quantum Simulations of Transmon Supercomputer
    IAS Series IAS Supercomputer simulations of transmon quantum computers quantum simulations of transmon Supercomputer 45 Supercomputer simulations of transmon quantum computers Dennis Willsch IAS Series IAS Series Band / Volume 45 Band / Volume 45 ISBN 978-3-95806-505-5 ISBN 978-3-95806-505-5 Dennis Willsch Schriften des Forschungszentrums Jülich IAS Series Band / Volume 45 Forschungszentrum Jülich GmbH Institute for Advanced Simulation (IAS) Jülich Supercomputing Centre (JSC) Supercomputer simulations of transmon quantum computers Dennis Willsch Schriften des Forschungszentrums Jülich IAS Series Band / Volume 45 ISSN 1868-8489 ISBN 978-3-95806-505-5 Bibliografsche Information der Deutschen Nationalbibliothek. Die Deutsche Nationalbibliothek verzeichnet diese Publikation in der Deutschen Nationalbibliografe; detaillierte Bibliografsche Daten sind im Internet über http://dnb.d-nb.de abrufbar. Herausgeber Forschungszentrum Jülich GmbH und Vertrieb: Zentralbibliothek, Verlag 52425 Jülich Tel.: +49 2461 61-5368 Fax: +49 2461 61-6103 [email protected] www.fz-juelich.de/zb Umschlaggestaltung: Grafsche Medien, Forschungszentrum Jülich GmbH Titelbild: Quantum Flagship/H.Ritsch Druck: Grafsche Medien, Forschungszentrum Jülich GmbH Copyright: Forschungszentrum Jülich 2020 Schriften des Forschungszentrums Jülich IAS Series, Band / Volume 45 D 82 (Diss. RWTH Aachen University, 2020) ISSN 1868-8489 ISBN 978-3-95806-505-5 Vollständig frei verfügbar über das Publikationsportal des Forschungszentrums Jülich (JuSER) unter www.fz-juelich.de/zb/openaccess. This is an Open Access publication distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Abstract We develop a simulator for quantum computers composed of superconducting transmon qubits.
    [Show full text]
  • Quantum Information Processing with Superconducting Circuits: a Review
    Quantum Information Processing with Superconducting Circuits: a Review G. Wendin Department of Microtechnology and Nanoscience - MC2, Chalmers University of Technology, SE-41296 Gothenburg, Sweden Abstract. During the last ten years, superconducting circuits have passed from being interesting physical devices to becoming contenders for near-future useful and scalable quantum information processing (QIP). Advanced quantum simulation experiments have been shown with up to nine qubits, while a demonstration of Quantum Supremacy with fifty qubits is anticipated in just a few years. Quantum Supremacy means that the quantum system can no longer be simulated by the most powerful classical supercomputers. Integrated classical-quantum computing systems are already emerging that can be used for software development and experimentation, even via web interfaces. Therefore, the time is ripe for describing some of the recent development of super- conducting devices, systems and applications. As such, the discussion of superconduct- ing qubits and circuits is limited to devices that are proven useful for current or near future applications. Consequently, the centre of interest is the practical applications of QIP, such as computation and simulation in Physics and Chemistry. Keywords: superconducting circuits, microwave resonators, Josephson junctions, qubits, quantum computing, simulation, quantum control, quantum error correction, superposition, entanglement arXiv:1610.02208v2 [quant-ph] 8 Oct 2017 Contents 1 Introduction 6 2 Easy and hard problems 8 2.1 Computational complexity . .9 2.2 Hard problems . .9 2.3 Quantum speedup . 10 2.4 Quantum Supremacy . 11 3 Superconducting circuits and systems 12 3.1 The DiVincenzo criteria (DV1-DV7) . 12 3.2 Josephson quantum circuits . 12 3.3 Qubits (DV1) .
    [Show full text]