T|Ket〉: a Retargetable Compiler for NISQ Devices
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
Introduction to Quantum Information Theory
Introduction to Quantum Information Theory Carlos Palazuelos Instituto de Ciencias Matemáticas (ICMAT) [email protected] Madrid, Spain March 2013 Contents Chapter 1. A comment on these notes 3 Chapter 2. Postulates of quantum mechanics 5 1. Postulate I and Postulate II 5 2. Postulate III 7 3. Postulate IV 9 Chapter 3. Some basic results 13 1. No-cloning theorem 13 2. Quantum teleportation 14 3. Superdense coding 15 Chapter 4. The density operators formalism 17 1. Postulates of quantum mechanics: The density operators formalism 17 2. Partial trace 19 3. The Schmidt decomposition and purifications 20 4. Definition of quantum channel and its classical capacity 21 Chapter 5. Quantum nonlocality 25 1. Bell’s result: Correlations in EPR 25 2. Tsirelson’s theorem and Grothendieck’s theorem 27 Chapter 6. Some notions about classical information theory 33 1. Shannon’s noiseless channel coding theorem 33 2. Conditional entropy and Fano’s inequality 35 3. Shannon’s noisy channel coding theorem: Random coding 38 Chapter 7. Quantum Shannon Theory 45 1. Von Neumann entropy 45 2. Schumacher’s compression theorem 48 3. Accessible Information and Holevo bound 55 4. Classical capacity of a quantum channel 58 5. A final comment about the regularization 61 Bibliography 63 1 CHAPTER 1 A comment on these notes These notes were elaborated during the first semester of 2013, while I was preparing a course on quantum information theory as a subject for the PhD programme: Investigación Matemática at Universidad Complutense de Madrid. The aim of this work is to present an accessible introduction to some topics in the field of quantum information theory for those people who do not have any background on the field. -
Efficient and Exact Quantum Compression
Efficient and Exact Quantum Compression a; ;1 b;2 John H. Reif ∗ , Sukhendu Chakraborty aDepartment of Computer Science, Duke University, Durham, NC 27708-0129 bDepartment of Computer Science, Duke University, Durham, NC 27708-0129 Abstract We present a divide and conquer based algorithm for optimal quantum compression / de- compression, using O(n(log4 n) log log n) elementary quantum operations . Our result pro- vides the first quasi-linear time algorithm for asymptotically optimal (in size and fidelity) quantum compression and decompression. We also outline the quantum gate array model to bring about this compression in a quantum computer. Our method uses various classical algorithmic tools to significantly improve the bound from the previous best known bound of O(n3)(R. Cleve, D. P. DiVincenzo, Schumacher's quantum data compression as a quantum computation, Phys. Rev. A, 54, 1996, 2636-2650) for this operation. Key words: Quantum computing, quantum compression, algorithm 1 Introduction 1.1 Quantum Computation Quantum Computation (QC) is a computing model that applies quantum me- chanics to do computation. (Computations and methods not making use of quantum mechanics will be termed classical). A single molecule (or collection of .particles and/or atoms) may have n degrees of freedom known as qubits. Associated with each fixed setting X of the n qubits to boolean values is a basis state denoted a . Quantum mechanics allows for a linear superposition of these basis states to j i ∗ Corresponding author. Email addresses: [email protected] ( John H. Reif), [email protected] (Sukhendu Chakraborty). 1 The work is supported by NSF EMT Grants CCF-0523555 and CCF-0432038. -
Quantum Computing in the NISQ Era and Beyond
Quantum Computing in the NISQ era and beyond John Preskill Institute for Quantum Information and Matter and Walter Burke Institute for Theoretical Physics, California Institute of Technology, Pasadena CA 91125, USA 30 July 2018 Noisy Intermediate-Scale Quantum (NISQ) technology will be available in the near future. Quantum computers with 50-100 qubits may be able to perform tasks which surpass the capabilities of today’s classical digital computers, but noise in quantum gates will limit the size of quantum circuits that can be executed reliably. NISQ devices will be useful tools for exploring many-body quantum physics, and may have other useful applications, but the 100-qubit quantum computer will not change the world right away — we should regard it as a significant step toward the more powerful quantum technologies of the future. Quantum technologists should continue to strive for more accurate quantum gates and, eventually, fully fault-tolerant quantum computing. 1 Introduction Now is an opportune time for a fruitful discussion among researchers, entrepreneurs, man- agers, and investors who share an interest in quantum computing. There has been a recent surge of investment by both large public companies and startup companies, a trend that has surprised many quantumists working in academia. While we have long recognized the commercial potential of quantum technology, this ramping up of industrial activity has happened sooner and more suddenly than most of us expected. In this article I assess the current status and future potential of quantum computing. Because quantum computing technology is so different from the information technology we use now, we have only a very limited ability to glimpse its future applications, or to project when these applications will come to fruition. -
Unitary Decomposition Implemented in the Openql Programming Language for Quantum Computation A.M
Unitary Decomposition Implemented in the OpenQL programming language for quantum computation A.M. Krol September 13th 36 Electrical Engineering, Mathematics and Computer Sciences LB 01.010 Snijderszaal Unitary Decomposition Implemented in the OpenQL programming language for quantum computation by A.M. Krol to obtain the degree of Master of Science at the Delft University of Technology, to be defended publicly on Friday September 13, 2019 at 15:00. Student number: 4292391 Project duration: November 20, 2018 – September 13, 2019 Thesis committee: Prof. dr. ir. K. Bertels, TU Delft, supervisor Dr. I. Ashraf, TU Delft Dr. M. Möller, TU Delft Dr. Z. Al-Ars TU Delft An electronic version of this thesis is available at http://repository.tudelft.nl/. Preface In this thesis, I explain my implementation of Unitary Decomposition in OpenQL. Unitary Decomposition is an algorithm for translating a unitary matrix into many small unitary matrices, which correspond to a circuit that can be executed on a quantum computer. It is implemented in the quantum programming framework of the QCA-group at TU Delft: OpenQL, a library for Python and C++. Unitary Decomposition is a necessary part in Quantum Associative Memory, an algorithm used in Quantum Genome Sequenc- ing. The implementation is faster than other known implementations, and generates 3 ∗ 2 ∗ (2 − 1) rotation gates for an n-qubit input gate. This is not the least-known nor the theoretical minimum amount, and there are some optimizations that can still be done to make it closer to these numbers. I would like to thank my supervisor, Prof. Koen Bertels, for the great supervision and insightful feedback. -
Quantum Computing
John H. Reif, Quantum Computing. Published in book “Bio-inspired and Nanoscale Integrated Computing”, Chapter 3, pp. 67-110 (edited by Mary Mehrnoosh Eshaghian-Wilner), Publisher: Wiley, Hoboken, NJ, USA, (February 2009). Chapter 3: Quantum Computing John H. Reif Department of Computer Science Duke University ‡ 3.0 Summary Quantum Computation (QC) is a type of computation where unitary and measurement operations are executed on linear superpositions of basis states. This paper provides a brief introduction to QC. We begin with a discussion of basic models for QC such as quantum TMs, quantum gates and circuits and related complexity results. We then discuss a number of topics in quan- tum information theory, including bounds for quantum communication and I/O complexity, methods for quantum data compression. and quantum er- ‡Surface address: Department of Computer Science, Duke University, Durham, NC 27708-0129. E-mail: [email protected]. This work has been supported by grants from NSF CCF-0432038 and CCF-0523555. A postscript preprint of this Chapter is available online at URL: http://www.cs.duke.edu/∼reif/paper/qsurvey/qsurvey.chapter.pdf. ror correction (that is, techniques for decreasing decoherence errors in QC), Furthermore, we enumerate a number of methodologies and technologies for doing QC. Finally, we discuss resource bounds for QC including bonds for processing time, energy and volume, particularly emphasizing challenges in determining volume bounds for observation apperatus. 3.1 Introduction 3.1.1 Reversible Computations Reversible Computations are computations where each state transformation is a reversible function, so that any computation can be reversed without loss of information. -
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. -
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) . -
Learning Nonlinear Input–Output Maps with Dissipative Quantum Systems
Quantum Information Processing (2019) 18:198 https://doi.org/10.1007/s11128-019-2311-9 Learning nonlinear input–output maps with dissipative quantum systems Jiayin Chen1 · Hendra I. Nurdin1 Received: 17 October 2018 / Accepted: 3 May 2019 / Published online: 15 May 2019 © Springer Science+Business Media, LLC, part of Springer Nature 2019 Abstract In this paper, we develop a theory of learning nonlinear input–output maps with fading memory by dissipative quantum systems, as a quantum counterpart of the theory of approximating such maps using classical dynamical systems. The theory identifies the properties required for a class of dissipative quantum systems to be universal, in that any input–output map with fading memory can be approximated arbitrarily closely by an element of this class. We then introduce an example class of dissipative quantum systems that is provably universal. Numerical experiments illustrate that with a small number of qubits, this class can achieve comparable performance to classical learning schemes with a large number of tunable parameters. Further numerical analysis sug- gests that the exponentially increasing Hilbert space presents a potential resource for dissipative quantum systems to surpass classical learning schemes for input–output maps. Keywords Machine learning with quantum systems · Dissipative quantum systems · Universality property · Reservoir computing · Nonlinear input–output maps · Fading memory maps · Nonlinear time series · Stone–Weierstrass theorem 1 Introduction We are in the midst of the noisy intermediate-scale quantum (NISQ) technology era [1], marked by noisy quantum computers consisting of roughly tens to hundreds of qubits. Currently, there is a substantial interest in early applications of these machines that can accelerate the development of practical quantum computers, akin to how the humble hearing aid stimulated the development of integrated circuit (IC) technology [2]. -
Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices
Tackling the Qubit Mapping Problem for NISQ-Era Quantum Devices Gushu Li Yufei Ding Yuan Xie University of California University of California University of California Santa Barbara, CA Santa Barbara, CA Santa Barbara, CA [email protected] [email protected] [email protected] Abstract 1 Introduction Due to little consideration in the hardware constraints, e.g., Quantum Computing (QC) has been rapidly growing in the limited connections between physical qubits to enable two- last few decades because of its potential in various important qubit gates, most quantum algorithms cannot be directly applications, including integer factorization [47], database executed on the Noisy Intermediate-Scale Quantum (NISQ) search [15], quantum simulation [36], etc. Recently, IBM, devices. Dynamically remapping logical qubits to physical Intel, and Google released their QC devices with 50, 49, and qubits in the compiler is needed to enable the two-qubit 72 qubits respectively [22, 23, 56]. IBM and Rigetti also pro- gates in the algorithm, which introduces additional oper- vide cloud QC services [18, 40], allowing more people to ations and inevitably reduces the fidelity of the algorithm. study real quantum hardware. We are expected to enter the Previous solutions in finding such remapping suffer from Noisy Intermediate-Scale Quantum (NISQ) era in the next high complexity, poor initial mapping quality, and limited few years [39], when QC devices with dozens to hundreds of flexibility and controllability. qubits will be available. Though the number of qubits is insuf- To address these drawbacks mentioned above, this paper ficient for Quantum Error Correction (QEC), .it is expected proposes a SWAP-based BidiREctional heuristic search al- that these devices will be used to solve real-world problems gorithm (SABRE), which is applicable to NISQ devices with beyond the capability of available classical computers [5, 38]. -
Chapter 5 Quantum Information Theory
Chapter 5 Quantum Information Theory Quantum information theory is a rich subject that could easily have occupied us all term. But because we are short of time (I’m anxious to move on to quantum computation), I won’t be able to cover this subject in as much depth as I would have liked. We will settle for a brisk introduction to some of the main ideas and results. The lectures will perhaps be sketchier than in the first term, with more hand waving and more details to be filled in through homework exercises. Perhaps this chapter should have been called “quantum information theory for the impatient.” Quantum information theory deals with four main topics: (1) Transmission of classical information over quantum channels (which we will discuss). (2) The tradeoff between acquisition of information about a quantum state and disturbance of the state (briefly discussed in Chapter 4 in connec- tion with quantum cryptography, but given short shrift here). (3) Quantifying quantum entanglement (which we will touch on briefly). (4) Transmission of quantum information over quantum channels. (We will discuss the case of a noiseless channel, but we will postpone discus- sion of the noisy channel until later, when we come to quantum error- correcting codes.) These topics are united by a common recurring theme: the interpretation and applications of the Von Neumann entropy. 1 2 CHAPTER 5. QUANTUM INFORMATION THEORY 5.1 Shannon for Dummies Before we can understand Von Neumann entropy and its relevance to quan- tum information, we must discuss Shannon entropy and its relevance to clas- sical information. -
Quantum Information Theory
Quantum Information Theory Jun-Ting Hsieh Bingbin Liu Stanford University Stanford University [email protected] [email protected] 1 Introduction In our current digital world, everything, from computing devices to communication channels, is represented as 0s and 1s. The “information” that these bits can present has been well studied in the field of classical information theory. For example, we know the capacity and limits of data compression and the rate of reliable communication over noisy channels. Recently, perhaps due to the excitement of quantum computation, these ideas in information theory have been extended to the quantum world, leading to the field of quantum information theory. In this paper, we would like to introduce the quantum counterparts of some of the key ideas we studied in the course, including the quantum entropy measure, quantum source encoding, and touch briefly on quantum channels. 1.1 How is quantum different than classical? Compared to the classical world, quantum mechanics has several “weird” properties that make things much more convoluted. Thus, we first introduce some major differences between quantum and classical information, which are the reasons that quantum information is so interesting yet harder to analyze. Refer to Section 2 for a more detailed introduction of quantum mechanics. Qubit. The quantum version of the classical bit is the qubit. A classical bit is either 0 or 1, while a qubit can be superposition of 0 and 1. This means that it is both 0 and 1, and if we perform a measurement we will get 0 and 1 with certain probabilities. Imagine that every bit in our computer is both 0 and 1 at the same time, and if we even try to read from it, we not only get a probabilistic answer but also collapse that qubit state! Entanglement.