Quantum Machine Learning
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Simulating Quantum Field Theory with a Quantum Computer
Simulating quantum field theory with a quantum computer John Preskill Lattice 2018 28 July 2018 This talk has two parts (1) Near-term prospects for quantum computing. (2) Opportunities in quantum simulation of quantum field theory. Exascale digital computers will advance our knowledge of QCD, but some challenges will remain, especially concerning real-time evolution and properties of nuclear matter and quark-gluon plasma at nonzero temperature and chemical potential. Digital computers may never be able to address these (and other) problems; quantum computers will solve them eventually, though I’m not sure when. The physics payoff may still be far away, but today’s research can hasten the arrival of a new era in which quantum simulation fuels progress in fundamental physics. Frontiers of Physics short distance long distance complexity Higgs boson Large scale structure “More is different” Neutrino masses Cosmic microwave Many-body entanglement background Supersymmetry Phases of quantum Dark matter matter Quantum gravity Dark energy Quantum computing String theory Gravitational waves Quantum spacetime particle collision molecular chemistry entangled electrons A quantum computer can simulate efficiently any physical process that occurs in Nature. (Maybe. We don’t actually know for sure.) superconductor black hole early universe Two fundamental ideas (1) Quantum complexity Why we think quantum computing is powerful. (2) Quantum error correction Why we think quantum computing is scalable. A complete description of a typical quantum state of just 300 qubits requires more bits than the number of atoms in the visible universe. Why we think quantum computing is powerful We know examples of problems that can be solved efficiently by a quantum computer, where we believe the problems are hard for classical computers. -
Arxiv:1803.04114V2 [Quant-Ph] 16 Nov 2018
Learning the quantum algorithm for state overlap Lukasz Cincio,1 Yiğit Subaşı,1 Andrew T. Sornborger,2 and Patrick J. Coles1 1Theoretical Division, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. 2Information Sciences, Los Alamos National Laboratory, Los Alamos, NM 87545, USA. Short-depth algorithms are crucial for reducing computational error on near-term quantum com- puters, for which decoherence and gate infidelity remain important issues. Here we present a machine-learning approach for discovering such algorithms. We apply our method to a ubiqui- tous primitive: computing the overlap Tr(ρσ) between two quantum states ρ and σ. The standard algorithm for this task, known as the Swap Test, is used in many applications such as quantum support vector machines, and, when specialized to ρ = σ, quantifies the Renyi entanglement. Here, we find algorithms that have shorter depths than the Swap Test, including one that has a constant depth (independent of problem size). Furthermore, we apply our approach to the hardware-specific connectivity and gate sets used by Rigetti’s and IBM’s quantum computers and demonstrate that the shorter algorithms that we derive significantly reduce the error - compared to the Swap Test - on these computers. I. INTRODUCTION hyperparameters, we optimize the algorithm in a task- oriented manner, i.e., by minimizing a cost function that quantifies the discrepancy between the algorithm’s out- Quantum supremacy [1] may be coming soon [2]. put and the desired output. The task is defined by a While it is an exciting time for quantum computing, de- training data set that exemplifies the desired computa- coherence and gate fidelity continue to be important is- tion. -
Quantum Machine Learning: Benefits and Practical Examples
Quantum Machine Learning: Benefits and Practical Examples Frank Phillipson1[0000-0003-4580-7521] 1 TNO, Anna van Buerenplein 1, 2595 DA Den Haag, The Netherlands [email protected] Abstract. A quantum computer that is useful in practice, is expected to be devel- oped in the next few years. An important application is expected to be machine learning, where benefits are expected on run time, capacity and learning effi- ciency. In this paper, these benefits are presented and for each benefit an example application is presented. A quantum hybrid Helmholtz machine use quantum sampling to improve run time, a quantum Hopfield neural network shows an im- proved capacity and a variational quantum circuit based neural network is ex- pected to deliver a higher learning efficiency. Keywords: Quantum Machine Learning, Quantum Computing, Near Future Quantum Applications. 1 Introduction Quantum computers make use of quantum-mechanical phenomena, such as superposi- tion and entanglement, to perform operations on data [1]. Where classical computers require the data to be encoded into binary digits (bits), each of which is always in one of two definite states (0 or 1), quantum computation uses quantum bits, which can be in superpositions of states. These computers would theoretically be able to solve certain problems much more quickly than any classical computer that use even the best cur- rently known algorithms. Examples are integer factorization using Shor's algorithm or the simulation of quantum many-body systems. This benefit is also called ‘quantum supremacy’ [2], which only recently has been claimed for the first time [3]. There are two different quantum computing paradigms. -
Quantum Noise in Quantum Optics: the Stochastic Schr\" Odinger
Quantum Noise in Quantum Optics: the Stochastic Schr¨odinger Equation Peter Zoller † and C. W. Gardiner ∗ † Institute for Theoretical Physics, University of Innsbruck, 6020 Innsbruck, Austria ∗ Department of Physics, Victoria University Wellington, New Zealand February 1, 2008 arXiv:quant-ph/9702030v1 12 Feb 1997 Abstract Lecture Notes for the Les Houches Summer School LXIII on Quantum Fluc- tuations in July 1995: “Quantum Noise in Quantum Optics: the Stochastic Schroedinger Equation” to appear in Elsevier Science Publishers B.V. 1997, edited by E. Giacobino and S. Reynaud. 0.1 Introduction Theoretical quantum optics studies “open systems,” i.e. systems coupled to an “environment” [1, 2, 3, 4]. In quantum optics this environment corresponds to the infinitely many modes of the electromagnetic field. The starting point of a description of quantum noise is a modeling in terms of quantum Markov processes [1]. From a physical point of view, a quantum Markovian description is an approximation where the environment is modeled as a heatbath with a short correlation time and weakly coupled to the system. In the system dynamics the coupling to a bath will introduce damping and fluctuations. The radiative modes of the heatbath also serve as input channels through which the system is driven, and as output channels which allow the continuous observation of the radiated fields. Examples of quantum optical systems are resonance fluorescence, where a radiatively damped atom is driven by laser light (input) and the properties of the emitted fluorescence light (output)are measured, and an optical cavity mode coupled to the outside radiation modes by a partially transmitting mirror. -
COVID-19 Detection on IBM Quantum Computer with Classical-Quantum Transfer Learning
medRxiv preprint doi: https://doi.org/10.1101/2020.11.07.20227306; this version posted November 10, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license . Turk J Elec Eng & Comp Sci () : { © TUB¨ ITAK_ doi:10.3906/elk- COVID-19 detection on IBM quantum computer with classical-quantum transfer learning Erdi ACAR1*, Ihsan_ YILMAZ2 1Department of Computer Engineering, Institute of Science, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey 2Department of Computer Engineering, Faculty of Engineering, C¸anakkale Onsekiz Mart University, C¸anakkale, Turkey Received: .201 Accepted/Published Online: .201 Final Version: ..201 Abstract: Diagnose the infected patient as soon as possible in the coronavirus 2019 (COVID-19) outbreak which is declared as a pandemic by the world health organization (WHO) is extremely important. Experts recommend CT imaging as a diagnostic tool because of the weak points of the nucleic acid amplification test (NAAT). In this study, the detection of COVID-19 from CT images, which give the most accurate response in a short time, was investigated in the classical computer and firstly in quantum computers. Using the quantum transfer learning method, we experimentally perform COVID-19 detection in different quantum real processors (IBMQx2, IBMQ-London and IBMQ-Rome) of IBM, as well as in different simulators (Pennylane, Qiskit-Aer and Cirq). By using a small number of data sets such as 126 COVID-19 and 100 Normal CT images, we obtained a positive or negative classification of COVID-19 with 90% success in classical computers, while we achieved a high success rate of 94-100% in quantum computers. -
Quantum Computation and Complexity Theory
Quantum computation and complexity theory Course given at the Institut fÈurInformationssysteme, Abteilung fÈurDatenbanken und Expertensysteme, University of Technology Vienna, Wintersemester 1994/95 K. Svozil Institut fÈur Theoretische Physik University of Technology Vienna Wiedner Hauptstraûe 8-10/136 A-1040 Vienna, Austria e-mail: [email protected] December 5, 1994 qct.tex Abstract The Hilbert space formalism of quantum mechanics is reviewed with emphasis on applicationsto quantum computing. Standardinterferomeric techniques are used to construct a physical device capable of universal quantum computation. Some consequences for recursion theory and complexity theory are discussed. hep-th/9412047 06 Dec 94 1 Contents 1 The Quantum of action 3 2 Quantum mechanics for the computer scientist 7 2.1 Hilbert space quantum mechanics ..................... 7 2.2 From single to multiple quanta Ð ªsecondº ®eld quantization ...... 15 2.3 Quantum interference ............................ 17 2.4 Hilbert lattices and quantum logic ..................... 22 2.5 Partial algebras ............................... 24 3 Quantum information theory 25 3.1 Information is physical ........................... 25 3.2 Copying and cloning of qbits ........................ 25 3.3 Context dependence of qbits ........................ 26 3.4 Classical versus quantum tautologies .................... 27 4 Elements of quantum computatability and complexity theory 28 4.1 Universal quantum computers ....................... 30 4.2 Universal quantum networks ........................ 31 4.3 Quantum recursion theory ......................... 35 4.4 Factoring .................................. 36 4.5 Travelling salesman ............................. 36 4.6 Will the strong Church-Turing thesis survive? ............... 37 Appendix 39 A Hilbert space 39 B Fundamental constants of physics and their relations 42 B.1 Fundamental constants of physics ..................... 42 B.2 Conversion tables .............................. 43 B.3 Electromagnetic radiation and other wave phenomena ......... -
Quantum Inductive Learning and Quantum Logic Synthesis
Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 2009 Quantum Inductive Learning and Quantum Logic Synthesis Martin Lukac Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Electrical and Computer Engineering Commons Let us know how access to this document benefits ou.y Recommended Citation Lukac, Martin, "Quantum Inductive Learning and Quantum Logic Synthesis" (2009). Dissertations and Theses. Paper 2319. https://doi.org/10.15760/etd.2316 This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. For more information, please contact [email protected]. QUANTUM INDUCTIVE LEARNING AND QUANTUM LOGIC SYNTHESIS by MARTIN LUKAC A dissertation submitted in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY in ELECTRICAL AND COMPUTER ENGINEERING. Portland State University 2009 DISSERTATION APPROVAL The abstract and dissertation of Martin Lukac for the Doctor of Philosophy in Electrical and Computer Engineering were presented January 9, 2009, and accepted by the dissertation committee and the doctoral program. COMMITTEE APPROVALS: Irek Perkowski, Chair GarrisoH-Xireenwood -George ^Lendaris 5artM ?teven Bleiler Representative of the Office of Graduate Studies DOCTORAL PROGRAM APPROVAL: Malgorza /ska-Jeske7~Director Electrical Computer Engineering Ph.D. Program ABSTRACT An abstract of the dissertation of Martin Lukac for the Doctor of Philosophy in Electrical and Computer Engineering presented January 9, 2009. Title: Quantum Inductive Learning and Quantum Logic Synhesis Since Quantum Computer is almost realizable on large scale and Quantum Technology is one of the main solutions to the Moore Limit, Quantum Logic Synthesis (QLS) has become a required theory and tool for designing Quantum Logic Circuits. -
Analysis of Nonlinear Dynamics in a Classical Transmon Circuit
Analysis of Nonlinear Dynamics in a Classical Transmon Circuit Sasu Tuohino B. Sc. Thesis Department of Physical Sciences Theoretical Physics University of Oulu 2017 Contents 1 Introduction2 2 Classical network theory4 2.1 From electromagnetic fields to circuit elements.........4 2.2 Generalized flux and charge....................6 2.3 Node variables as degrees of freedom...............7 3 Hamiltonians for electric circuits8 3.1 LC Circuit and DC voltage source................8 3.2 Cooper-Pair Box.......................... 10 3.2.1 Josephson junction.................... 10 3.2.2 Dynamics of the Cooper-pair box............. 11 3.3 Transmon qubit.......................... 12 3.3.1 Cavity resonator...................... 12 3.3.2 Shunt capacitance CB .................. 12 3.3.3 Transmon Lagrangian................... 13 3.3.4 Matrix notation in the Legendre transformation..... 14 3.3.5 Hamiltonian of transmon................. 15 4 Classical dynamics of transmon qubit 16 4.1 Equations of motion for transmon................ 16 4.1.1 Relations with voltages.................. 17 4.1.2 Shunt resistances..................... 17 4.1.3 Linearized Josephson inductance............. 18 4.1.4 Relation with currents................... 18 4.2 Control and read-out signals................... 18 4.2.1 Transmission line model.................. 18 4.2.2 Equations of motion for coupled transmission line.... 20 4.3 Quantum notation......................... 22 5 Numerical solutions for equations of motion 23 5.1 Design parameters of the transmon................ 23 5.2 Resonance shift at nonlinear regime............... 24 6 Conclusions 27 1 Abstract The focus of this thesis is on classical dynamics of a transmon qubit. First we introduce the basic concepts of the classical circuit analysis and use this knowledge to derive the Lagrangians and Hamiltonians of an LC circuit, a Cooper-pair box, and ultimately we derive Hamiltonian for a transmon qubit. -
Nearest Centroid Classification on a Trapped Ion Quantum Computer
www.nature.com/npjqi ARTICLE OPEN Nearest centroid classification on a trapped ion quantum computer ✉ Sonika Johri1 , Shantanu Debnath1, Avinash Mocherla2,3,4, Alexandros SINGK2,3,5, Anupam Prakash2,3, Jungsang Kim1 and Iordanis Kerenidis2,3,6 Quantum machine learning has seen considerable theoretical and practical developments in recent years and has become a promising area for finding real world applications of quantum computers. In pursuit of this goal, here we combine state-of-the-art algorithms and quantum hardware to provide an experimental demonstration of a quantum machine learning application with provable guarantees for its performance and efficiency. In particular, we design a quantum Nearest Centroid classifier, using techniques for efficiently loading classical data into quantum states and performing distance estimations, and experimentally demonstrate it on a 11-qubit trapped-ion quantum machine, matching the accuracy of classical nearest centroid classifiers for the MNIST handwritten digits dataset and achieving up to 100% accuracy for 8-dimensional synthetic data. npj Quantum Information (2021) 7:122 ; https://doi.org/10.1038/s41534-021-00456-5 INTRODUCTION Thus, one might hope that noisy quantum computers are 1234567890():,; Quantum technologies promise to revolutionize the future of inherently better suited for machine learning computations than information and communication, in the form of quantum for other types of problems that need precise computations like computing devices able to communicate and process massive factoring or search problems. amounts of data both efficiently and securely using quantum However, there are significant challenges to be overcome to resources. Tremendous progress is continuously being made both make QML practical. -
Signing Perfect Currency Bonds
Signing Perfect Currency Bonds Subhayan Roy Moulick∗ and Prasanta K. Panigrahiy Indian Institute of Science Education and Research Kolkata, Mohanpur 741246, West Bengal, India (Dated: May 23, 2019) We propose the idea of a Quantum Cheque Scheme, a cryptographic protocol in which any legiti- mate client of a trusted bank can issue a cheque, that cannot be counterfeited or altered in anyway, and can be verified by a bank or any of its branches. We formally define a Quantum Cheque and present the first Unconditionally Secure Quantum Cheque Scheme and show it to be secure against any no-signaling adversary. The proposed Quantum Cheque Scheme can been perceived as the quantum analog of Electronic Data Interchange, as an alternate for current e-Payment Gateways. PACS numbers: 03.67.Dd, 03.67.Hk, 03.67.Ac I. INTRODUCTION in addition to security against counterfeiters, owing its origin to the pioneering works of Mosca and Stebila [11]. Replication of classical information is a significant nui- They proposed a scheme based on blind quantum com- sance in copy-protection. Any physical entity created putation that required a verifier to do an obfuscated ver- classically can be, in principle, copied. Currency bonds, ification with the bank and learn only the validity of the printed on textile and paper, are no exception, and any quantum coin. This however is a private key protocol adversary, given sufficient time and resources, can be and requires communication with the bank. able to counterfeit currency bonds. However, the quan- In this paper we propose the idea of Quantum Cheques tum regime can circumvent this problem, exploiting the and present a construction of an Quantum Cheque `No Cloning Theorem' [1], and pave way for unforgeable Scheme with Perfect Security against any No-Signaling Quantum Currency that are impossible to counterfeit adversary. -
Explorations in Quantum Neural Networks with Intermediate Measurements
ESANN 2020 proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. Online event, 2-4 October 2020, i6doc.com publ., ISBN 978-2-87587-074-2. Available from http://www.i6doc.com/en/. Explorations in Quantum Neural Networks with Intermediate Measurements Lukas Franken and Bogdan Georgiev ∗Fraunhofer IAIS - Research Center for ML and ML2R Schloss Birlinghoven - 53757 Sankt Augustin Abstract. In this short note we explore a few quantum circuits with the particular goal of basic image recognition. The models we study are inspired by recent progress in Quantum Convolution Neural Networks (QCNN) [12]. We present a few experimental results, where we attempt to learn basic image patterns motivated by scaling down the MNIST dataset. 1 Introduction The recent demonstration of Quantum Supremacy [1] heralds the advent of the Noisy Intermediate-Scale Quantum (NISQ) [2] technology, where signs of supe- riority of quantum over classical machines in particular tasks may be expected. However, one should keep in mind the limitations of NISQ-devices when study- ing and developing quantum-algorithmic solutions - among other things, these include limits on the number of gates and qubits. At the same time the interaction of quantum computing and machine learn- ing is growing, with a vast amount of literature and new results. To name a few applications, the well-known HHL algorithm [3], quantum phase estimation [5] and inner products speed-up techniques lead to further advances in Support Vector Machines [4] and Principal Component Analysis [6, 7]. Intensive progress and ongoing research has also been made towards quantum analogues of Neural Networks (QNN) [8, 9, 10]. -
Quantum Memories for Continuous Variable States of Light in Atomic Ensembles
Quantum Memories for Continuous Variable States of Light in Atomic Ensembles Gabriel H´etet A thesis submitted for the degree of Doctor of Philosophy at The Australian National University October, 2008 ii Declaration This thesis is an account of research undertaken between March 2005 and June 2008 at the Department of Physics, Faculty of Science, Australian National University (ANU), Can- berra, Australia. This research was supervised by Prof. Ping Koy Lam (ANU), Prof. Hans- Albert Bachor (ANU), Dr. Ben Buchler (ANU) and Dr. Joseph Hope (ANU). Unless otherwise indicated, the work present herein is my own. None of the work presented here has ever been submitted for any degree at this or any other institution of learning. Gabriel H´etet October 2008 iii iv Acknowledgments I would naturally like to start by thanking my principal supervisors. Ping Koy Lam is the person I owe the most. I really appreciated his approachability, tolerance, intuition, knowledge, enthusiasm and many other qualities that made these three years at the ANU a great time. Hans Bachor, for helping making this PhD in Canberra possible and giving me the opportunity to work in this dynamic research environment. I always enjoyed his friendliness and general views and interpretations of physics. Thanks also to both of you for the opportunity you gave me to travel and present my work to international conferences. This thesis has been a rich experience thanks to my other two supervisors : Joseph Hope and Ben Buchler. I would like to thank Joe for invaluable help on the theory and computer side and Ben for the efficient moments he spent improving the set-up and other innumerable tips that polished up the work presented here, including proof reading this thesis.