Transmon-QD-ETH.Pdf
Total Page:16
File Type:pdf, Size:1020Kb

Load more
Recommended publications
-
Theory and Application of Fermi Pseudo-Potential in One Dimension
Theory and application of Fermi pseudo-potential in one dimension The Harvard community has made this article openly available. Please share how this access benefits you. Your story matters Citation Wu, Tai Tsun, and Ming Lun Yu. 2002. “Theory and Application of Fermi Pseudo-Potential in One Dimension.” Journal of Mathematical Physics 43 (12): 5949–76. https:// doi.org/10.1063/1.1519940. Citable link http://nrs.harvard.edu/urn-3:HUL.InstRepos:41555821 Terms of Use This article was downloaded from Harvard University’s DASH repository, and is made available under the terms and conditions applicable to Other Posted Material, as set forth at http:// nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of- use#LAA CERN-TH/2002-097 Theory and application of Fermi pseudo-potential in one dimension Tai Tsun Wu Gordon McKay Laboratory, Harvard University, Cambridge, Massachusetts, U.S.A., and Theoretical Physics Division, CERN, Geneva, Switzerland and Ming Lun Yu 41019 Pajaro Drive, Fremont, California, U.S.A.∗ Abstract The theory of interaction at one point is developed for the one-dimensional Schr¨odinger equation. In analog with the three-dimensional case, the resulting interaction is referred to as the Fermi pseudo-potential. The dominant feature of this one-dimensional problem comes from the fact that the real line becomes disconnected when one point is removed. The general interaction at one point is found to be the sum of three terms, the well-known delta-function potential arXiv:math-ph/0208030v1 21 Aug 2002 and two Fermi pseudo-potentials, one odd under space reflection and the other even. -
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 . -
A Theoretical Study of Quantum Memories in Ensemble-Based Media
A theoretical study of quantum memories in ensemble-based media Karl Bruno Surmacz St. Hugh's College, Oxford A thesis submitted to the Mathematical and Physical Sciences Division for the degree of Doctor of Philosophy in the University of Oxford Michaelmas Term, 2007 Atomic and Laser Physics, University of Oxford i A theoretical study of quantum memories in ensemble-based media Karl Bruno Surmacz, St. Hugh's College, Oxford Michaelmas Term 2007 Abstract The transfer of information from flying qubits to stationary qubits is a fundamental component of many quantum information processing and quantum communication schemes. The use of photons, which provide a fast and robust platform for encoding qubits, in such schemes relies on a quantum memory in which to store the photons, and retrieve them on-demand. Such a memory can consist of either a single absorber, or an ensemble of absorbers, with a ¤-type level structure, as well as other control ¯elds that a®ect the transfer of the quantum signal ¯eld to a material storage state. Ensembles have the advantage that the coupling of the signal ¯eld to the medium scales with the square root of the number of absorbers. In this thesis we theoretically study the use of ensembles of absorbers for a quantum memory. We characterize a general quantum memory in terms of its interaction with the signal and control ¯elds, and propose a ¯gure of merit that measures how well such a memory preserves entanglement. We derive an analytical expression for the entanglement ¯delity in terms of fluctuations in the stochastic Hamiltonian parameters, and show how this ¯gure could be measured experimentally. -
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. -
Operation-Induced Decoherence by Nonrelativistic Scattering from a Quantum Memory
INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF PHYSICS A: MATHEMATICAL AND GENERAL J. Phys. A: Math. Gen. 39 (2006) 11567–11581 doi:10.1088/0305-4470/39/37/015 Operation-induced decoherence by nonrelativistic scattering from a quantum memory D Margetis1 and J M Myers2 1 Department of Mathematics, and Institute for Physical Science and Technology, University of Maryland, College Park, MD 20742, USA 2 Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA E-mail: [email protected] and [email protected] Received 16 June 2006, in final form 7 August 2006 Published 29 August 2006 Online at stacks.iop.org/JPhysA/39/11567 Abstract Quantum computing involves transforming the state of a quantum memory. We view this operation as performed by transmitting nonrelativistic (massive) particles that scatter from the memory. By using a system of (1+1)- dimensional, coupled Schrodinger¨ equations with point interaction and narrow- band incoming pulse wavefunctions, we show how the outgoing pulse becomes entangled with a two-state memory. This effect necessarily induces decoherence, i.e., deviations of the memory content from a pure state. We describe incoming pulses that minimize this decoherence effect under a constraint on the duration of their interaction with the memory. PACS numbers: 03.65.−w, 03.67.−a, 03.65.Nk, 03.65.Yz, 03.67.Lx 1. Introduction Quantum computing [1–4] relies on a sequence of operations, each of which transforms a pure quantum state to, ideally, another pure state. These states describe a physical system, usually called memory. A central problem in quantum computing is decoherence, in which the pure state degrades to a mixed state, with deleterious effects for the computation. -
Experimental Kernel-Based Quantum Machine Learning in Finite Feature
www.nature.com/scientificreports OPEN Experimental kernel‑based quantum machine learning in fnite feature space Karol Bartkiewicz1,2*, Clemens Gneiting3, Antonín Černoch2*, Kateřina Jiráková2, Karel Lemr2* & Franco Nori3,4 We implement an all‑optical setup demonstrating kernel‑based quantum machine learning for two‑ dimensional classifcation problems. In this hybrid approach, kernel evaluations are outsourced to projective measurements on suitably designed quantum states encoding the training data, while the model training is processed on a classical computer. Our two-photon proposal encodes data points in a discrete, eight-dimensional feature Hilbert space. In order to maximize the application range of the deployable kernels, we optimize feature maps towards the resulting kernels’ ability to separate points, i.e., their “resolution,” under the constraint of fnite, fxed Hilbert space dimension. Implementing these kernels, our setup delivers viable decision boundaries for standard nonlinear supervised classifcation tasks in feature space. We demonstrate such kernel-based quantum machine learning using specialized multiphoton quantum optical circuits. The deployed kernel exhibits exponentially better scaling in the required number of qubits than a direct generalization of kernels described in the literature. Many contemporary computational problems (like drug design, trafc control, logistics, automatic driving, stock market analysis, automatic medical examination, material engineering, and others) routinely require optimiza- tion over huge amounts of data1. While these highly demanding problems can ofen be approached by suitable machine learning (ML) algorithms, in many relevant cases the underlying calculations would last prohibitively long. Quantum ML (QML) comes with the promise to run these computations more efciently (in some cases exponentially faster) by complementing ML algorithms with quantum resources. -
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 -
Valleytronics: Opportunities, Challenges, and Paths Forward
REVIEW Valleytronics www.small-journal.com Valleytronics: Opportunities, Challenges, and Paths Forward Steven A. Vitale,* Daniel Nezich, Joseph O. Varghese, Philip Kim, Nuh Gedik, Pablo Jarillo-Herrero, Di Xiao, and Mordechai Rothschild The workshop gathered the leading A lack of inversion symmetry coupled with the presence of time-reversal researchers in the field to present their symmetry endows 2D transition metal dichalcogenides with individually latest work and to participate in honest and addressable valleys in momentum space at the K and K′ points in the first open discussion about the opportunities Brillouin zone. This valley addressability opens up the possibility of using the and challenges of developing applications of valleytronic technology. Three interactive momentum state of electrons, holes, or excitons as a completely new para- working sessions were held, which tackled digm in information processing. The opportunities and challenges associated difficult topics ranging from potential with manipulation of the valley degree of freedom for practical quantum and applications in information processing and classical information processing applications were analyzed during the 2017 optoelectronic devices to identifying the Workshop on Valleytronic Materials, Architectures, and Devices; this Review most important unresolved physics ques- presents the major findings of the workshop. tions. The primary product of the work- shop is this article that aims to inform the reader on potential benefits of valleytronic 1. Background devices, on the state-of-the-art in valleytronics research, and on the challenges to be overcome. We are hopeful this document The Valleytronics Materials, Architectures, and Devices Work- will also serve to focus future government-sponsored research shop, sponsored by the MIT Lincoln Laboratory Technology programs in fruitful directions. -
Measurement of Superconducting Qubits and Causality Alexander Korotkov University of California, Riverside
Michigan State Univ., 09/21/15 Measurement of superconducting qubits and causality Alexander Korotkov University of California, Riverside Outline: Introduction (causality, POVM measurement) Circuit QED setup for measurement of superconducting qubits (transmons, Xmons) Qubit evolution due to measurement in circuit QED (simple and better theories) Experiments Alexander Korotkov University of California, Riverside Causality principle in quantum mechanics objects a and b observers A and B (and C) observers have “free will”; A B they can choose an action A choice made by observer A can affect light a b evolution of object b “back in time” cones time However, this retroactive control cannot pass space “useful” information to B (no signaling) C Randomness saves causality (even C Our focus: continuous cannot predict result of A measurement) |0 collapse Ensemble-averaged evolution of object b cannot depend on actions of observer A |1 Alexander Korotkov University of California, Riverside Werner Heisenberg Books: Physics and Philosophy: The Revolution in Modern Science Philosophical Problems of Quantum Physics The Physicist's Conception of Nature Across the Frontiers Niels Bohr Immanuel Kant (1724-1804), German philosopher Critique of pure reason (materialism, but not naive materialism) Nature - “Thing-in-itself” (noumenon, not phenomenon) Humans use “concepts (categories) of understanding”; make sense of phenomena, but never know noumena directly A priori: space, time, causality A naïve philosophy should not be a roadblock for good physics, -
Fundamental Aspects of Solving Quantum Problems with Machine Learning
Fundamental aspects of solving quantum problems with machine learning Hsin-Yuan (Robert) Huang, Richard Kueng, Michael Broughton, Masoud Mohseni, Ryan Babbush, Sergio Boixo, Hartmut Neven, Jarrod McClean and John Preskill Google AI Quantum Institute of Quantum Information and Matter (IQIM), Caltech Johannes Kepler University Linz [1] Information-theoretic bounds on quantum advantage in machine learning, arXiv:2101.02464. [2] Power of data in quantum machine learning, arXiv:2011.01938. [3] Provable machine learning algorithms for quantum many-body problems, In preparation. Motivation • Machine learning (ML) has received great attention in the quantum community these days. Classical ML Enhancing ML for quantum physics/chemistry with quantum computers The goal : The goal : Solve challenging quantum Design quantum ML algorithms many-body problems that yield better than significant advantage traditional classical algorithms over any classical algorithm “Solving the quantum many-body problem with artificial neural networks.” Science 355.6325 (2017): 602-606. "Learning phase transitions by confusion." Nature Physics 13.5 (2017): 435-439. "Supervised learning with quantum-enhanced feature spaces." Nature 567.7747 (2019): 209-212. Motivation • Yet, many fundamental questions remain to be answered. Classical ML Enhancing ML for quantum physics/chemistry with quantum computers The question : The question : How can ML be more useful What are the advantages of than non-ML algorithms? quantum ML in general? “Solving the quantum many-body problem with artificial neural networks.” Science 355.6325 (2017): 602-606. "Learning phase transitions by confusion." Nature Physics 13.5 (2017): 435-439. "Supervised learning with quantum-enhanced feature spaces." Nature 567.7747 (2019): 209-212. General Setting • In this work, we focus on training an ML model to predict x ↦ fℰ(x) = Tr(Oℰ(|x⟩⟨x|)), where x is a classical input, ℰ is an unknown CPTP map, and O is an observable. -
Quantum Secure Direct Communication with Quantum Memory
Quantum secure direct communication with quantum memory Wei Zhang1,3, Dong-Sheng Ding1,3*, Yu-Bo Sheng2, Lan Zhou2, Bao-Sen Shi1,3† and Guang-Can Guo1,3 1Key Laboratory of Quantum Information, Chinese Academy of Sciences, University of Science and Technology of China, Hefei, Anhui 230026, China 2Key Laboratory of Broadband Wireless Communication and Sensor Network Technology, Nanjing University of Posts and Telecommunications, Ministry of Education, Nanjing 210003, China 3Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science and Technology of China, Hefei, Anhui 230026, China Corresponding authors:* [email protected] [email protected] †[email protected] Quantum communication provides an absolute security advantage, and it has been widely developed over the past 30 years. As an important branch of quantum communication, quantum secure direct communication (QSDC) promotes high security and instantaneousness in communication through directly transmitting messages over a quantum channel. The full implementation of a quantum protocol always requires the ability to control the transfer of a message effectively in the time domain; thus, it is essential to combine QSDC with quantum memory to accomplish the communication task. In this paper, we report the experimental demonstration of QSDC with state-of-the-art atomic quantum memory for the first time in principle. We used the polarization degrees of freedom of photons as the information carrier, and the fidelity of entanglement decoding was verified as approximately 90%. Our work completes a fundamental step toward practical QSDC and demonstrates a potential application for long-distance quantum communication in a quantum network. The importance of information and communication security is increasing rapidly as the Internet becomes indispensable in modern society. -
Quantum Computing: Lecture Notes
Quantum Computing: Lecture Notes Ronald de Wolf Preface These lecture notes were formed in small chunks during my “Quantum computing” course at the University of Amsterdam, Feb-May 2011, and compiled into one text thereafter. Each chapter was covered in a lecture of 2 45 minutes, with an additional 45-minute lecture for exercises and × homework. The first half of the course (Chapters 1–7) covers quantum algorithms, the second half covers quantum complexity (Chapters 8–9), stuff involving Alice and Bob (Chapters 10–13), and error-correction (Chapter 14). A 15th lecture about physical implementations and general outlook was more sketchy, and I didn’t write lecture notes for it. These chapters may also be read as a general introduction to the area of quantum computation and information from the perspective of a theoretical computer scientist. While I made an effort to make the text self-contained and consistent, it may still be somewhat rough around the edges; I hope to continue polishing and adding to it. Comments & constructive criticism are very welcome, and can be sent to [email protected] Attribution and acknowledgements Most of the material in Chapters 1–6 comes from the first chapter of my PhD thesis [71], with a number of additions: the lower bound for Simon, the Fourier transform, the geometric explanation of Grover. Chapter 7 is newly written for these notes, inspired by Santha’s survey [62]. Chapters 8 and 9 are largely new as well. Section 3 of Chapter 8, and most of Chapter 10 are taken (with many changes) from my “quantum proofs” survey paper with Andy Drucker [28].