IBM Q: Hardware, Software, Future
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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 . -
Optimization of Transmon Design for Longer Coherence Time
Optimization of transmon design for longer coherence time Simon Burkhard Semester thesis Spring semester 2012 Supervisor: Dr. Arkady Fedorov ETH Zurich Quantum Device Lab Prof. Dr. A. Wallraff Abstract Using electrostatic simulations, a transmon qubit with new shape and a large geometrical size was designed. The coherence time of the qubits was increased by a factor of 3-4 to about 4 µs. Additionally, a new formula for calculating the coupling strength of the qubit to a transmission line resonator was obtained, allowing reasonably accurate tuning of qubit parameters prior to production using electrostatic simulations. 2 Contents 1 Introduction 4 2 Theory 5 2.1 Coplanar waveguide resonator . 5 2.1.1 Quantum mechanical treatment . 5 2.2 Superconducting qubits . 5 2.2.1 Cooper pair box . 6 2.2.2 Transmon . 7 2.3 Resonator-qubit coupling (Jaynes-Cummings model) . 9 2.4 Effective transmon network . 9 2.4.1 Calculation of CΣ ............................... 10 2.4.2 Calculation of β ............................... 10 2.4.3 Qubits coupled to 2 resonators . 11 2.4.4 Charge line and flux line couplings . 11 2.5 Transmon relaxation time (T1) ........................... 11 3 Transmon simulation 12 3.1 Ansoft Maxwell . 12 3.1.1 Convergence of simulations . 13 3.1.2 Field integral . 13 3.2 Optimization of transmon parameters . 14 3.3 Intermediate transmon design . 15 3.4 Final transmon design . 15 3.5 Comparison of the surface electric field . 17 3.6 Simple estimate of T1 due to coupling to the charge line . 17 3.7 Simulation of the magnetic flux through the split junction . -
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. -
Quantum Computers
Quantum Computers ERIK LIND. PROFESSOR IN NANOELECTRONICS Erik Lind EIT, Lund University 1 Disclaimer Erik Lind EIT, Lund University 2 Quantum Computers Classical Digital Computers Why quantum computers? Basics of QM • Spin-Qubits • Optical qubits • Superconducting Qubits • Topological Qubits Erik Lind EIT, Lund University 3 Classical Computing • We build digital electronics using CMOS • Boolean Logic • Classical Bit – 0/1 (Defined as a voltage level 0/+Vdd) +vDD +vDD +vDD Inverter NAND Logic is built by connecting gates Erik Lind EIT, Lund University 4 Classical Computing • We build digital electronics using CMOS • Boolean Logic • Classical Bit – 0/1 • Very rubust! • Modern CPU – billions of transistors (logic and memory) • We can keep a logical state for years • We can easily copy a bit • Mainly through irreversible computing However – some problems are hard to solve on a classical computer Erik Lind EIT, Lund University 5 Computing Complexity Searching an unsorted list Factoring large numbers Grovers algo. – Shor’s Algorithm BQP • P – easy to solve and check on a 푛 classical computer ~O(N) • NP – easy to check – hard to solve on a classical computer ~O(2N) P P • BQP – easy to solve on a quantum computer. ~O(N) NP • BQP is larger then P • Some problems in P can be more efficiently solved by a quantum computer Simulating quantum systems Note – it is still not known if 푃 = 푁푃. It is very hard to build a quantum computer… Erik Lind EIT, Lund University 6 Basics of Quantum Mechanics A state is a vector (ray) in a complex Hilbert Space Dimension of space – possible eigenstates of the state For quantum computation – two dimensional Hilbert space ۧ↓ Example - Spin ½ particle - ȁ↑ۧ 표푟 Spin can either be ‘up’ or ‘down’ Erik Lind EIT, Lund University 7 Basics of Quantum Mechanics • A state of a system is a vector (ray) in a complex vector (Hilbert) Space. -
Qubic: an Open Source FPGA-Based Control and Measurement System for Superconducting Quantum Information Processors
1 QubiC: An open source FPGA-based control and measurement system for superconducting quantum information processors Yilun Xu,1 Gang Huang,1 Jan Balewski,1 Ravi Naik,2 Alexis Morvan,1 Bradley Mitchell,2 Kasra Nowrouzi,1 David I. Santiago,1 and Irfan Siddiqi1;2 1Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA 2University of California at Berkeley, Berkeley, CA 94720, USA Corresponding author: Gang Huang (email: [email protected]) Abstract—As quantum information processors grow in quan- for general purpose test and measurement applications, and tum bit (qubit) count and functionality, the control and measure- typically cannot keep pace both in terms of footprint and cost ment system becomes a limiting factor to large scale extensibility. as quantum system complexity increases [6]. A customized To tackle this challenge and keep pace with rapidly evolving classical control requirements, full control stack access is essential quantum engineering solution rooted in extensible primitives to system level optimization. We design a modular FPGA (field- is needed for the quantum computing community. programmable gate array) based system called QubiC to control The field-programmable gate array (FPGA) architecture and measure a superconducting quantum processing unit. The allows for customized solutions capable of growing and evolv- system includes room temperature electronics hardware, FPGA ing with the field [7]. Several FPGA frameworks have been gateware, and engineering software. A prototype hardware mod- ule is assembled from several commercial off-the-shelf evaluation developed for quantum control and measurement [8]–[11]. boards and in-house developed circuit boards. Gateware and Nevertheless, current FPGA-based control systems are not software are designed to implement basic qubit control and fully open to the broader quantum community so it is hard measurement protocols. -
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. -
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. -
Mapping Quantum Algorithms in a Crossbar Architecture
Mapping QUANTUM ALGORITHMS IN A CROSSBAR ARCHITECTURE Alejandro MorAIS Mapping quantum algorithms in a crossbar architecture by Alejandro Morais to obtain the degree of Master of Science at the Delft University of Technology, to be defended publicly on the 25th of September 2019. Student number: 4747240 Project duration: November 1, 2018 – July 1, 2019 Thesis committee: Dr. ir. C.G. Almudever, QCA, TU Delft Dr. ir. Z. Al-Ars, CE, TU Delft Dr. ir. F. Sebastiano, AQUA, TU Delft Dr. ir. M. Veldhorst, QuTech, TU Delft Supervisor: Dr. ir. C.G. Almudever, QCA, TU Delft An electronic version of this thesis is available at https://repository.tudelft.nl/. Abstract In recent years, Quantum Computing has gone from theory to a promising reality, leading to quantum chips that in a near future might be able to exceed the computational power of any current supercomputer. For this to happen, there are some problems that must be overcome. For example, in a quantum processor, qubits are usually arranged in a 2D architecture with limited connectivity between them and in which only nearest-neighbour interactions are allowed. This restricts the execution of two-qubit gates and requires qubit to be moved to adjacent positions. Quantum algorithms, which are described as quantum circuits, neglect the quantum chip constraints and therefore cannot be directly executed. This is known as the mapping problem. This thesis focuses on the problem of mapping quantum algorithms into a quantum chip based on spin qubits, called the crossbar architecture. In this project we have developed the required compiler support (mapping) for making quantum circuits executable on the crossbar architecture based on the tools provided by OpenQL. -
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. -
Superconducting Transmon Machines
Quantum Computing Hardware Platforms: Superconducting Transmon Machines • CSC591/ECE592 – Quantum Computing • Fall 2020 • Professor Patrick Dreher • Research Professor, Department of Computer Science • Chief Scientist, NC State IBM Q Hub 3 November 2020 Superconducting Transmon Quantum Computers 1 5 November 2020 Patrick Dreher Outline • Introduction – Digital versus Quantum Computation • Conceptual design for superconducting transmon QC hardware platforms • Construction of qubits with electronic circuits • Superconductivity • Josephson junction and nonlinear LC circuits • Transmon design with example IBM chip layout • Working with a superconducting transmon hardware platform • Quantum Mechanics of Two State Systems - Rabi oscillations • Performance characteristics of superconducting transmon hardware • Construct the basic CNOT gate • Entangled transmons and controlled gates • Appendices • References • Quantum mechanical solution of the 2 level system 3 November 2020 Superconducting Transmon Quantum Computers 2 5 November 2020 Patrick Dreher Digital Computation Hardware Platforms Based on a Base2 Mathematics • Design principle for a digital computer is built on base2 mathematics • Identify voltage changes in electrical circuits that map base2 math into a “zero” or “one” corresponding to an on/off state • Build electrical circuits from simple on/off states that apply these basic rules to construct digital computers 3 November 2020 Superconducting Transmon Quantum Computers 3 5 November 2020 Patrick Dreher From Previous Lectures It was -
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. -
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,