Scalable Designs for Quasiparticle-Poisoning-Protected Topological Quantum Computation with Majorana Zero Modes
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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. -
Direct Dispersive Monitoring of Charge Parity in Offset-Charge
PHYSICAL REVIEW APPLIED 12, 014052 (2019) Direct Dispersive Monitoring of Charge Parity in Offset-Charge-Sensitive Transmons K. Serniak,* S. Diamond, M. Hays, V. Fatemi, S. Shankar, L. Frunzio, R.J. Schoelkopf, and M.H. Devoret† Department of Applied Physics, Yale University, New Haven, Connecticut 06520, USA (Received 29 March 2019; revised manuscript received 20 June 2019; published 26 July 2019) A striking characteristic of superconducting circuits is that their eigenspectra and intermode coupling strengths are well predicted by simple Hamiltonians representing combinations of quantum-circuit ele- ments. Of particular interest is the Cooper-pair-box Hamiltonian used to describe the eigenspectra of transmon qubits, which can depend strongly on the offset-charge difference across the Josephson element. Notably, this offset-charge dependence can also be observed in the dispersive coupling between an ancil- lary readout mode and a transmon fabricated in the offset-charge-sensitive (OCS) regime. We utilize this effect to achieve direct high-fidelity dispersive readout of the joint plasmon and charge-parity state of an OCS transmon, which enables efficient detection of charge fluctuations and nonequilibrium-quasiparticle dynamics. Specifically, we show that additional high-frequency filtering can extend the charge-parity life- time of our device by 2 orders of magnitude, resulting in a significantly improved energy relaxation time T1 ∼ 200 μs. DOI: 10.1103/PhysRevApplied.12.014052 I. INTRODUCTION charge states, like a usual transmon but with measurable offset-charge dispersion of the transition frequencies The basic building blocks of quantum circuits—e.g., between eigenstates, like a Cooper-pair box. This defines capacitors, inductors, and nonlinear elements such as what we refer to as the offset-charge-sensitive (OCS) Josephson junctions [1] and electromechanical trans- transmon regime. -
A Scanning Transmon Qubit for Strong Coupling Circuit Quantum Electrodynamics
ARTICLE Received 8 Mar 2013 | Accepted 10 May 2013 | Published 7 Jun 2013 DOI: 10.1038/ncomms2991 A scanning transmon qubit for strong coupling circuit quantum electrodynamics W. E. Shanks1, D. L. Underwood1 & A. A. Houck1 Like a quantum computer designed for a particular class of problems, a quantum simulator enables quantitative modelling of quantum systems that is computationally intractable with a classical computer. Superconducting circuits have recently been investigated as an alternative system in which microwave photons confined to a lattice of coupled resonators act as the particles under study, with qubits coupled to the resonators producing effective photon–photon interactions. Such a system promises insight into the non-equilibrium physics of interacting bosons, but new tools are needed to understand this complex behaviour. Here we demonstrate the operation of a scanning transmon qubit and propose its use as a local probe of photon number within a superconducting resonator lattice. We map the coupling strength of the qubit to a resonator on a separate chip and show that the system reaches the strong coupling regime over a wide scanning area. 1 Department of Electrical Engineering, Princeton University, Olden Street, Princeton 08550, New Jersey, USA. Correspondence and requests for materials should be addressed to W.E.S. (email: [email protected]). NATURE COMMUNICATIONS | 4:1991 | DOI: 10.1038/ncomms2991 | www.nature.com/naturecommunications 1 & 2013 Macmillan Publishers Limited. All rights reserved. ARTICLE NATURE COMMUNICATIONS | DOI: 10.1038/ncomms2991 ver the past decade, the study of quantum physics using In this work, we describe a scanning superconducting superconducting circuits has seen rapid advances in qubit and demonstrate its coupling to a superconducting CPWR Osample design and measurement techniques1–3. -
CS286.2 Lectures 5-6: Introduction to Hamiltonian Complexity, QMA-Completeness of the Local Hamiltonian Problem
CS286.2 Lectures 5-6: Introduction to Hamiltonian Complexity, QMA-completeness of the Local Hamiltonian problem Scribe: Jenish C. Mehta The Complexity Class BQP The complexity class BQP is the quantum analog of the class BPP. It consists of all languages that can be decided in quantum polynomial time. More formally, Definition 1. A language L 2 BQP if there exists a classical polynomial time algorithm A that ∗ maps inputs x 2 f0, 1g to quantum circuits Cx on n = poly(jxj) qubits, where the circuit is considered a sequence of unitary operators each on 2 qubits, i.e Cx = UTUT−1...U1 where each 2 2 Ui 2 L C ⊗ C , such that: 2 i. Completeness: x 2 L ) Pr(Cx accepts j0ni) ≥ 3 1 ii. Soundness: x 62 L ) Pr(Cx accepts j0ni) ≤ 3 We say that the circuit “Cx accepts jyi” if the first output qubit measured in Cxjyi is 0. More j0i specifically, letting P1 = j0ih0j1 be the projection of the first qubit on state j0i, j0i 2 Pr(Cx accepts jyi) =k (P1 ⊗ In−1)Cxjyi k2 The Complexity Class QMA The complexity class QMA (or BQNP, as Kitaev originally named it) is the quantum analog of the class NP. More formally, Definition 2. A language L 2 QMA if there exists a classical polynomial time algorithm A that ∗ maps inputs x 2 f0, 1g to quantum circuits Cx on n + q = poly(jxj) qubits, such that: 2q i. Completeness: x 2 L ) 9jyi 2 C , kjyik2 = 1, such that Pr(Cx accepts j0ni ⊗ 2 jyi) ≥ 3 2q 1 ii. -
Taking the Quantum Leap with Machine Learning
Introduction Quantum Algorithms Current Research Conclusion Taking the Quantum Leap with Machine Learning Zack Barnes University of Washington Bellevue College Mathematics and Physics Colloquium Series [email protected] January 15, 2019 Zack Barnes University of Washington UW Quantum Machine Learning Introduction Quantum Algorithms Current Research Conclusion Overview 1 Introduction What is Quantum Computing? What is Machine Learning? Quantum Power in Theory 2 Quantum Algorithms HHL Quantum Recommendation 3 Current Research Quantum Supremacy(?) 4 Conclusion Zack Barnes University of Washington UW Quantum Machine Learning Introduction Quantum Algorithms Current Research Conclusion What is Quantum Computing? \Quantum computing focuses on studying the problem of storing, processing and transferring information encoded in quantum mechanical systems.\ [Ciliberto, Carlo et al., 2018] Unit of quantum information is the qubit, or quantum binary integer. Zack Barnes University of Washington UW Quantum Machine Learning Supervised Uses labeled examples to predict future events Unsupervised Not classified or labeled Introduction Quantum Algorithms Current Research Conclusion What is Machine Learning? \Machine learning is the scientific study of algorithms and statistical models that computer systems use to progressively improve their performance on a specific task.\ (Wikipedia) Zack Barnes University of Washington UW Quantum Machine Learning Uses labeled examples to predict future events Unsupervised Not classified or labeled Introduction Quantum -
Interactive Proofs for Quantum Computations
Innovations in Computer Science 2010 Interactive Proofs For Quantum Computations Dorit Aharonov Michael Ben-Or Elad Eban School of Computer Science, The Hebrew University of Jerusalem, Israel [email protected] [email protected] [email protected] Abstract: The widely held belief that BQP strictly contains BPP raises fundamental questions: Upcoming generations of quantum computers might already be too large to be simulated classically. Is it possible to experimentally test that these systems perform as they should, if we cannot efficiently compute predictions for their behavior? Vazirani has asked [21]: If computing predictions for Quantum Mechanics requires exponential resources, is Quantum Mechanics a falsifiable theory? In cryptographic settings, an untrusted future company wants to sell a quantum computer or perform a delegated quantum computation. Can the customer be convinced of correctness without the ability to compare results to predictions? To provide answers to these questions, we define Quantum Prover Interactive Proofs (QPIP). Whereas in standard Interactive Proofs [13] the prover is computationally unbounded, here our prover is in BQP, representing a quantum computer. The verifier models our current computational capabilities: it is a BPP machine, with access to few qubits. Our main theorem can be roughly stated as: ”Any language in BQP has a QPIP, and moreover, a fault tolerant one” (providing a partial answer to a challenge posted in [1]). We provide two proofs. The simpler one uses a new (possibly of independent interest) quantum authentication scheme (QAS) based on random Clifford elements. This QPIP however, is not fault tolerant. Our second protocol uses polynomial codes QAS due to Ben-Or, Cr´epeau, Gottesman, Hassidim, and Smith [8], combined with quantum fault tolerance and secure multiparty quantum computation techniques. -
Quantum Supremacy
Quantum Supremacy Practical QS: perform some computational task on a well-controlled quantum device, which cannot be simulated in a reasonable time by the best-known classical algorithms and hardware. Theoretical QS: perform a computational task efficiently on a quantum device, and prove that task cannot be efficiently classically simulated. Since proving seems to be beyond the capabilities of our current civilization, we lower the standards for theoretical QS. One seeks to provide formal evidence that classical simulation is unlikely. For example: 3-SAT is NP-complete, so it cannot be efficiently classical solved unless P = NP. Theoretical QS: perform a computational task efficiently on a quantum device, and prove that task cannot be efficiently classically simulated unless “the polynomial Heierarchy collapses to the 3nd level.” Quantum Supremacy A common feature of QS arguments is that they consider sampling problems, rather than decision problems. They allow us to characterize the complexity of sampling measurements of quantum states. Which is more difficult: Task A: deciding if a circuit outputs 1 with probability at least 2/3s, or at most 1/3s Task B: sampling from the output of an n-qubit circuit in the computational basis Sampling from distributions is generically more difficult than approximating observables, since we can use samples to estimate observables, but not the other way around. One can imagine quantum systems whose local observables are easy to classically compute, but for which sampling the full state is computationally complex. By moving from decision problems to sampling problems, we make the task of classical simulation much more difficult. -
Quantum Algorithms for Classical Lattice Models
Home Search Collections Journals About Contact us My IOPscience Quantum algorithms for classical lattice models This content has been downloaded from IOPscience. Please scroll down to see the full text. 2011 New J. Phys. 13 093021 (http://iopscience.iop.org/1367-2630/13/9/093021) View the table of contents for this issue, or go to the journal homepage for more Download details: IP Address: 147.96.14.15 This content was downloaded on 16/12/2014 at 15:54 Please note that terms and conditions apply. New Journal of Physics The open–access journal for physics Quantum algorithms for classical lattice models G De las Cuevas1,2,5, W Dür1, M Van den Nest3 and M A Martin-Delgado4 1 Institut für Theoretische Physik, Universität Innsbruck, Technikerstraße 25, A-6020 Innsbruck, Austria 2 Institut für Quantenoptik und Quanteninformation der Österreichischen Akademie der Wissenschaften, Innsbruck, Austria 3 Max-Planck-Institut für Quantenoptik, Hans-Kopfermann-Strasse 1, D-85748 Garching, Germany 4 Departamento de Física Teórica I, Universidad Complutense, 28040 Madrid, Spain E-mail: [email protected] New Journal of Physics 13 (2011) 093021 (35pp) Received 15 April 2011 Published 9 September 2011 Online at http://www.njp.org/ doi:10.1088/1367-2630/13/9/093021 Abstract. We give efficient quantum algorithms to estimate the partition function of (i) the six-vertex model on a two-dimensional (2D) square lattice, (ii) the Ising model with magnetic fields on a planar graph, (iii) the Potts model on a quasi-2D square lattice and (iv) the Z2 lattice gauge theory on a 3D square lattice. -
Readout Rebalancing for Near Term Quantum Computers
Readout Rebalancing for Near Term Quantum Computers Rebecca Hicks,1, ∗ Christian W. Bauer,2, † and Benjamin Nachman2, ‡ 1Physics Department, University of California, Berkeley, Berkeley, CA 94720, USA 2Physics Division, Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA (Dated: October 16, 2020) Readout errors are a significant source of noise for near term intermediate scale quantum computers. Mismeasuring a qubit as a |1i when it should be |0i occurs much less often than mismeasuring a qubit as a |0i when it should have been |1i. We make the simple observation that one can improve the readout fidelity of quantum computers by applying targeted X gates prior to performing a measurement. These X gates are placed so that the expected number of qubits in the |1i state is minimized. Classical post processing can undo the effect of the X gates so that the expectation value of any observable is unchanged. We show that the statistical uncertainty following readout error corrections is smaller when using readout rebalancing. The statistical advantage is circuit- and computer-dependent, and is demonstrated for the W state, a Grover search, and for a Gaussian state. The benefit in statistical precision is most pronounced (and nearly a factor of two in some cases) when states with many qubits in the excited state have high probability. I. INTRODUCTION the simple observation that one can improve the readout fidelity of quantum computers by applying targeted X Quantum computers hold great promise for a variety of gates prior to performing a measurement. These X gates scientific and industrial applications. However, existing are placed so that the expected number of qubits in the noisy intermediate-scale quantum (NISQ) computers [1] |1i state is minimized. -
Higher Levels of the Transmon Qubit
Higher Levels of the Transmon Qubit MASSACHUSETTS INSTITUTE OF TECHNirLOGY by AUG 15 2014 Samuel James Bader LIBRARIES Submitted to the Department of Physics in partial fulfillment of the requirements for the degree of Bachelor of Science in Physics at the MASSACHUSETTS INSTITUTE OF TECHNOLOGY June 2014 @ Samuel James Bader, MMXIV. All rights reserved. The author hereby grants to MIT permission to reproduce and to distribute publicly paper and electronic copies of this thesis document in whole or in part in any medium now known or hereafter created. Signature redacted Author........ .. ----.-....-....-....-....-.....-....-......... Department of Physics Signature redacted May 9, 201 I Certified by ... Terr P rlla(nd Professor of Electrical Engineering Signature redacted Thesis Supervisor Certified by ..... ..................... Simon Gustavsson Research Scientist Signature redacted Thesis Co-Supervisor Accepted by..... Professor Nergis Mavalvala Senior Thesis Coordinator, Department of Physics Higher Levels of the Transmon Qubit by Samuel James Bader Submitted to the Department of Physics on May 9, 2014, in partial fulfillment of the requirements for the degree of Bachelor of Science in Physics Abstract This thesis discusses recent experimental work in measuring the properties of higher levels in transmon qubit systems. The first part includes a thorough overview of transmon devices, explaining the principles of the device design, the transmon Hamiltonian, and general Cir- cuit Quantum Electrodynamics concepts and methodology. The second part discusses the experimental setup and methods employed in measuring the higher levels of these systems, and the details of the simulation used to explain and predict the properties of these levels. Thesis Supervisor: Terry P. Orlando Title: Professor of Electrical Engineering Thesis Supervisor: Simon Gustavsson Title: Research Scientist 3 4 Acknowledgments I would like to express my deepest gratitude to Dr. -
In Situ Quantum Control Over Superconducting Qubits
! In situ quantum control over superconducting qubits Anatoly Kulikov M.Sc. A thesis submitted for the degree of Doctor of Philosophy at The University of Queensland in 2020 School of Mathematics and Physics ARC Centre of Excellence for Engineered Quantum Systems (EQuS) ABSTRACT In the last decade, quantum information processing has transformed from a field of mostly academic research to an applied engineering subfield with many commercial companies an- nouncing strategies to achieve quantum advantage and construct a useful universal quantum computer. Continuing efforts to improve qubit lifetime, control techniques, materials and fab- rication methods together with exploring ways to scale up the architecture have culminated in the recent achievement of quantum supremacy using a programmable superconducting proces- sor { a major milestone in quantum computing en route to useful devices. Marking the point when for the first time a quantum processor can outperform the best classical supercomputer, it heralds a new era in computer science, technology and information processing. One of the key developments enabling this transition to happen is the ability to exert more precise control over quantum bits and the ability to detect and mitigate control errors and imperfections. In this thesis, ways to efficiently control superconducting qubits are explored from the experimental viewpoint. We introduce a state-of-the-art experimental machinery enabling one to perform one- and two-qubit gates focusing on the technical aspect and outlining some guidelines for its efficient operation. We describe the software stack from the time alignment of control pulses and triggers to the data processing organisation. We then bring in the standard qubit manipulation and readout methods and proceed to describe some of the more advanced optimal control and calibration techniques. -
QIP 2010 Tutorial and Scientific Programmes
QIP 2010 15th – 22nd January, Zürich, Switzerland Tutorial and Scientific Programmes asymptotically large number of channel uses. Such “regularized” formulas tell us Friday, 15th January very little. The purpose of this talk is to give an overview of what we know about 10:00 – 17:10 Jiannis Pachos (Univ. Leeds) this need for regularization, when and why it happens, and what it means. I will Why should anyone care about computing with anyons? focus on the quantum capacity of a quantum channel, which is the case we understand best. This is a short course in topological quantum computation. The topics to be covered include: 1. Introduction to anyons and topological models. 15:00 – 16:55 Daniel Nagaj (Slovak Academy of Sciences) 2. Quantum Double Models. These are stabilizer codes, that can be described Local Hamiltonians in quantum computation very much like quantum error correcting codes. They include the toric code This talk is about two Hamiltonian Complexity questions. First, how hard is it to and various extensions. compute the ground state properties of quantum systems with local Hamiltonians? 3. The Jones polynomials, their relation to anyons and their approximation by Second, which spin systems with time-independent (and perhaps, translationally- quantum algorithms. invariant) local interactions could be used for universal computation? 4. Overview of current state of topological quantum computation and open Aiming at a participant without previous understanding of complexity theory, we will discuss two locally-constrained quantum problems: k-local Hamiltonian and questions. quantum k-SAT. Learning the techniques of Kitaev and others along the way, our first goal is the understanding of QMA-completeness of these problems.