QS3 2017: Getting Started with the IBM Quantum Experience

Total Page：16

File Type：pdf, Size：1020Kb

QS3 2017: Getting started with the IBM Quantum Experience All users: start exploring the QX website . Get an account if you don’t have one already: – https://quantumexperience.ng.bluemix.net/qx/signup . Study the User Guide, which aims to provide a complete, self‐contained introduction . Try out the Composer and run some experiments “Serious” users: get extended usability (including eventual access to the 16‐qubit device) by using the Quantum Information Software Kit (QISKit) API Two methods: A) run python on your laptop; B) run python on a cloud‐hosted site . Method A (good if running Linux and/or have experience with Python and Git): – Install Anaconda: https://www.continuum.io/downloads – (Optional but recommended) Install Git: https://git‐scm.com/download/ – Get the QISKit SDK: . If you got Git, open a command prompt and navigate to the folder where you’d like to install QISKit, then run the following command: git clone http://github.com/IBM/qiskit‐sdk‐py . If you didn’t get Git, visit http://github.com/IBM/qiskit‐sdk‐py in a browser, download the repository, and unzip to the desired folder – Perform setup: . Linux users: run make env from inside the qiskit‐sdk‐py folder . Windows users: run these commands from the qiskit‐sdk‐py folder: . conda create -y -n QISKitenv python=3 pip . activate QISKitenv . pip install -r requires.txt . Copy qiskit‐sdk‐py/tutorial/Qconfig.py.default to qiskit‐sdk‐py/Qconfig.py, then uncomment the APItoken line in the copied file and put your token (obtained from the Quantum Experience website under “My Account” “Personal Access Token”) in the space between the quotes – Start running: . Linux users: navigate to the qiskit‐sdk‐py folder and type make run . Windows users: run these commands from the qiskit‐sdk‐py folder: . activate QISKitenv . cd tutorial . jupyter notebook index.ipynb . Method B (good if running Windows, want ease of access from multiple computers, and/or new to Python and Git): – Create an account at http://datascientistworkbench.com – Launch the Jupyter Notebook interface (may take up to ~10 mins!) – Paste http://ibm.biz/QX_DSWB in the box at the upper right, locate QX_DSWB under “My Data,” rename it to QX_DSWB.ipynb (click the “>” to the left of the name), locate QX_DSWB.ipynb under “My Notebooks,” click to launch the notebook, and follow the directions in the notebook. – To run the code in each cell, click inside it and type Shift+Enter .

Copy Link

Recommended publications

Implementing Grover's Algorithm on the IBM Quantum Computers
Implementing Grover’s Algorithm on the IBM Quantum Computers Aamir Mandviwalla*, Keita Ohshiro* and Bo Ji Abstract—This paper focuses on testing the current viability of Our goal in this paper is to study how close we are using quantum computers for the processing of data-driven tasks hardware-wise to realistically being able to exploit the poten- fueled by emerging data science applications. We test the publicly tial beneﬁts of quantum algorithms. Prior work is rather limited available IBM quantum computers using Grover’s algorithm, a well-known quantum search algorithm, to obtain a baseline for this subject. There exist articles proclaiming that the age for the general evaluations of these quantum devices and to of quantum computing is only a year or two away along with investigate the impacts of various factors such as number of less positive articles that do not expect major advancements quantum bits (or qubits), qubit choice, and device choice. The for at least ten years [7], [8]. Instead of providing a prediction main contributions of this paper include a new 4-qubit imple- for the future, in this paper we aim to evaluate the accuracy mentation of Grover’s algorithm and test results showing the current capabilities of quantum computers. Our study indicates of quantum computers that are publicly available now. that quantum computers can currently only be used accurately To accomplish this goal, we run tests using Grover’s al- for solving simple problems with very small amounts of data. gorithm implemented in a variety of different ways on the There are also notable differences between different selections of publicly available IBM computers: IBM Q 5 Tenerife (5-qubit the qubits in the implementation design and between different chip) and IBM Q 16 Ruschlikon¨ (16-qubit chip) [9].
[Show full text]
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. Córcoles1, 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]
Quantum Computing for Undergraduates: a True STEAM Case
Lat. Am. J. Sci. Educ. 6, 22030 (2019) Latin American Journal of Science Education www.lajse.org Quantum Computing for Undergraduates: a true STEAM case a b c César B. Cevallos , Manuel Álvarez Alvarado , and Celso L. Ladera a Instituto de Ciencias Básicas, Dpto. de Física, Universidad Técnica de Manabí, Portoviejo, Provincia de Manabí, Ecuador b Facultad de Ingeniería en Electricidad y Computación, Escuela Politécnica del Litoral, Guayaquil. Ecuador c Departamento de Física, Universidad Simón Bolívar, Valle de Sartenejas, Caracas 1089, Venezuela A R T I C L E I N F O A B S T R A C T Received: Agosto 15, 2019 The first quantum computers of 5-20 superconducting qubits are now available for free Accepted: September 20, 2019 through the Cloud for anyone who wants to implement arrays of logical gates, and eventually Available on-line: Junio 6, 2019 to program advanced computer algorithms. The latter to be eventually used in solving Combinatorial Optimization problems, in Cryptography or for cracking complex Keywords: STEAM, quantum computational chemistry problems, which cannot be either programmed or solved using computer, engineering curriculum, classical computers based on present semiconductor electronics. Moreover, Quantum physics curriculum. Advantage, i.e. computational power beyond that of conventional computers seems to be within our reach in less than one year from now (June 2018). It does seem unlikely that these E-mail addresses: new fast computers, based on quantum mechanics and superconducting technology, will ever [email protected] become laptop-like. Yet, within a decade or less, their physics, technology and programming [email protected] will forcefully become part, of the undergraduate curriculae of Physics, Electronics, Material [email protected] Science, and Computer Science: it is indeed a subject that embraces Sciences, Advanced Technologies and even Art e.g.
[Show full text]
3Rd Summit on Advances in Programming Languages (SNAPL 2019)
3rd Summit on Advances in Programming Languages SNAPL 2019, May 16–17, 2019, Providence, RI, USA Edited by Benjamin S. Lerner Rastislav Bodík Shriram Krishnamurthi L I P I c s – Vo l . 136 – SNAPL 2019 w w w . d a g s t u h l . d e / l i p i c s Editors Benjamin S. Lerner Northeastern University, USA [email protected] Rastislav Bodík University of California Berkeley, USA [email protected] Shriram Krishnamurthi Brown University, USA [email protected] ACM Classification 2012 Software and its engineering → General programming languages; Software and its engineering → Semantics ISBN 978-3-95977-113-9 Published online and open access by Schloss Dagstuhl – Leibniz-Zentrum für Informatik GmbH, Dagstuhl Publishing, Saarbrücken/Wadern, Germany. Online available at https://www.dagstuhl.de/dagpub/978-3-95977-113-9. Publication date July, 2019 Bibliographic information published by the Deutsche Nationalbibliothek The Deutsche Nationalbibliothek lists this publication in the Deutsche Nationalbibliografie; detailed bibliographic data are available in the Internet at https://portal.dnb.de. License This work is licensed under a Creative Commons Attribution 3.0 Unported license (CC-BY 3.0): https://creativecommons.org/licenses/by/3.0/legalcode. In brief, this license authorizes each and everybody to share (to copy, distribute and transmit) the work under the following conditions, without impairing or restricting the authors’ moral rights: Attribution: The work must be attributed to its authors. The copyright is retained by the corresponding authors. Digital Object Identifier: 10.4230/LIPIcs.SNAPL.2019.0 ISBN 978-3-95977-113-9 ISSN 1868-8969 https://www.dagstuhl.de/lipics 0:iii LIPIcs – Leibniz International Proceedings in Informatics LIPIcs is a series of high-quality conference proceedings across all fields in informatics.
[Show full text]
Special Theme Quantum Computing
Number 112 January 2018 ERCIM NEWS www.ercim.eu Special theme Quantum Computing Also in this issue: Research and Innovation: Computers that negotiate on our behalf Editorial Information Contents ERCIM News is the magazine of ERCIM. Published quar - terly, it reports on joint actions of the ERCIM partners, and aims to reflect the contribution made by ERCIM to the European Community in Information Technology and Applied Mathematics. Through short articles and news items, it pro - vides a forum for the exchange of information between the JoINt ERCIM ACtIoNS SPECIAL tHEME institutes and also with the wider scientific community. This issue has a circulation of about 6,000 printed copies and is 4 Foreword from the President The special theme section “Quantum also available online. Computing” has been coordinated by 4 Jos Baeten, Elected President of Jop Briët (CWI) and Simon Perdrix ERCIM News is published by ERCIM EEIG ERCIM AISBL (CNRS, LORIA) BP 93, F-06902 Sophia Antipolis Cedex, France Tel: +33 4 9238 5010, E-mail: [email protected] 5 Tim Baarslag Winner of the 2017 8 Quantum Computation and Director: Philipp Hoschka, ISSN 0926-4981 Cor Baayen Young Researcher Information - Introduction to the Award Special Theme Contributions by Jop Briët (CWI) and Simon Contributions should be submitted to the local editor of your 6 ERCIM Cor Baayen Award 2017 Perdrix (CNRS, LORIA) country 7 ERCIM Established Working 10 How Classical Beings Can Test Copyrightnotice Group on Blockchain Technology Quantum Devices All authors, as identified in each article, retain copyright of their by Stacey Jeffery (CWI) work. ERCIM News is licensed under a Creative Commons 7 HORIZON 2020 Project Attribution 4.0 International License (CC-BY).
[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]
Lawrence Berkeley National Laboratory Recent Work
Lawrence Berkeley National Laboratory Recent Work Title Simulating Quantum Materials with Digital Quantum Computers Permalink https://escholarship.org/uc/item/4zg9w1vj Authors Bassman, Lindsay Urbanek, Miroslav Metcalf, Mekena et al. Publication Date 2021-01-21 Peer reviewed eScholarship.org Powered by the California Digital Library University of California Simulating Quantum Materials with Digital Quantum Computers Lindsay Bassman, Miroslav Urbanek, Mekena Metcalf, Jonathan Carter Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA E-mail: [email protected] Alexander F. Kemper Department of Physics, North Carolina State University, Raleigh, North Carolina 27695, USA Wibe de Jong Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA Abstract. Quantum materials exhibit a wide array of exotic phenomena and practically useful properties. A better understanding of these materials can provide deeper insights into fundamental physics in the quantum realm as well as advance technology for entertainment, healthcare, and sustainability. The emergence of digital quantum computers (DQCs), which can eﬃciently perform quantum simulations that are otherwise intractable on classical computers, provides a promising path forward for testing and analyzing the remarkable, and often counter-intuitive, behavior of quantum materials. Equipped with these new tools, scientists from diverse domains are racing towards achieving physical quantum advantage (i.e., using a quantum computer to learn new physics with a computation that cannot feasibly be run on any classical computer). The aim of this review, therefore, is to provide a summary of progress made towards this goal that is accessible to scientists across the physical sciences. We will ﬁrst review the available technology and algorithms, and detail the myriad ways arXiv:2101.08836v1 [quant-ph] 21 Jan 2021 to represent materials on quantum computers.
[Show full text]