ICTS Programme on Quantum Information Processing 5 December 2007 to 4 January 2008
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Learning Programs for the Quantum Computer
Learning Programs For The Quantum Computer by Olivia{Linda Enciu Bachelor of Science (Honours), University of Toronto, 2010 A thesis presented to Ryerson University in partial fulfillment of the requirements for the degree of Master of Science in the Program of Computer Science Toronto, Ontario, Canada, 2014 c Olivia{Linda Enciu 2014 AUTHOR'S DECLARATION FOR ELECTRONIC SUBMISSION OF A THESIS I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I authorize Ryerson University to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize Ryerson University to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. I understand that my thesis may be made electronically available to the public. iii Learning Programs For The Quantum Computer Master of Science 2014 Olivia{Linda Enciu Computer Science Ryerson University Abstract Manual quantum programming is generally difficult for humans, due to the often hard-to-grasp properties of quantum mechanics and quantum computers. By outlining the target (or desired) behaviour of a particular quantum program, the task of program- ming can be turned into a search and optimization problem. A flexible evolutionary technique known as genetic programming may then be used as an aid in the search for quantum programs. In this work a genetic programming approach uses an estimation of distribution algorithm (EDA) to learn the probability distribution of optimal solution(s), given some target behaviour of a quantum program. -
High Energy Physics Quantum Computing
High Energy Physics Quantum Computing Quantum Information Science in High Energy Physics at the Large Hadron Collider PI: O.K. Baker, Yale University Unraveling the quantum structure of QCD in parton shower Monte Carlo generators PI: Christian Bauer, Lawrence Berkeley National Laboratory Co-PIs: Wibe de Jong and Ben Nachman (LBNL) The HEP.QPR Project: Quantum Pattern Recognition for Charged Particle Tracking PI: Heather Gray, Lawrence Berkeley National Laboratory Co-PIs: Wahid Bhimji, Paolo Calafiura, Steve Farrell, Wim Lavrijsen, Lucy Linder, Illya Shapoval (LBNL) Neutrino-Nucleus Scattering on a Quantum Computer PI: Rajan Gupta, Los Alamos National Laboratory Co-PIs: Joseph Carlson (LANL); Alessandro Roggero (UW), Gabriel Purdue (FNAL) Particle Track Pattern Recognition via Content-Addressable Memory and Adiabatic Quantum Optimization PI: Lauren Ice, Johns Hopkins University Co-PIs: Gregory Quiroz (Johns Hopkins); Travis Humble (Oak Ridge National Laboratory) Towards practical quantum simulation for High Energy Physics PI: Peter Love, Tufts University Co-PIs: Gary Goldstein, Hugo Beauchemin (Tufts) High Energy Physics (HEP) ML and Optimization Go Quantum PI: Gabriel Perdue, Fermilab Co-PIs: Jim Kowalkowski, Stephen Mrenna, Brian Nord, Aris Tsaris (Fermilab); Travis Humble, Alex McCaskey (Oak Ridge National Lab) Quantum Machine Learning and Quantum Computation Frameworks for HEP (QMLQCF) PI: M. Spiropulu, California Institute of Technology Co-PIs: Panagiotis Spentzouris (Fermilab), Daniel Lidar (USC), Seth Lloyd (MIT) Quantum Algorithms for Collider Physics PI: Jesse Thaler, Massachusetts Institute of Technology Co-PI: Aram Harrow, Massachusetts Institute of Technology Quantum Machine Learning for Lattice QCD PI: Boram Yoon, Los Alamos National Laboratory Co-PIs: Nga T. T. Nguyen, Garrett Kenyon, Tanmoy Bhattacharya and Rajan Gupta (LANL) Quantum Information Science in High Energy Physics at the Large Hadron Collider O.K. -
QUANTUM COMPUTER and QUANTUM ALGORITHM for TRAVELLING SALESMAN PROBLEM Utpal Roy, Sanchita Pal Chawdhury, Susmita Nayek
ISSN: 0974-3308, VOL. 2, NO. 1, JUNE 2009 © SRIMCA 54 QUANTUM COMPUTER AND QUANTUM ALGORITHM FOR TRAVELLING SALESMAN PROBLEM Utpal Roy, Sanchita Pal Chawdhury, Susmita Nayek ABSTRACT Depending upon the extraordinary power of Quantum Computing Algorithms various branches like Quantum Cryptography, Quantum Information Technology, Quantum Teleportation have emerged [1-4]. It is thought that this power of Quantum Computing Algorithms can also be successfully applied to many combinatorial optimization problems. In this article, a class of combinatorial optimization problem is chosen as case study under Quantum Computing. These problems are widely believed to be unsolvable in polynomial time. Mostly it provides suboptimal solutions in finite time using best known classical algorithms. Travelling Salesman Problem (TSP) is one such problem to be studied here. A great deal of effort has already been devoted towards devising efficient algorithms that can solve the problem [5-18]. Moreover, the methods of finding solutions for the TSP with Artificial Neural Network and Genetic Algorithms [5-8] do not provide the exact solution to the problems for all the cases, excepting a few. A successful attempt has been made to have a deterministic solution for TSP by applying the power of Quantum Computing Algorithm. Keywords: Quantum Computing, Travelling Salesman Problem, Quantum algorithms. 1. INTRODUCTION A quantum computer is a device that can arbitrarily manipulate the quantum state of a part or itself. The field of quantum computation is largely a body of theoretical promises for some impressively fast algorithms which could be executed on quantum computers. The field of quantum computing has advanced remarkably in the past few years since Shor [1] presented his quantum mechanical algorithm for efficient prime factorization of very large numbers, potentially providing an exponential speedup over the fastness on known classical algorithm. -
Arxiv:1606.09225V1
Quintuple: a Python 5-qubit quantum computer simulator to facilitate cloud quantum computing Christine Corbett Morana,b,∗ aNSF AAPF California Institute of Technology, TAPIR, 1207 E. California Blvd. Pasadena, CA 91125 bUniversity of Chicago, 2016 SPT Winterover Scientist, Amundsen-Scott South Pole Station, Antarctica Abstract In May 2016 IBM released access to its 5-qubit quantum computer to the scientific commu- nity, its “IBM Quantum Experience”[1] since acquiring over 25,000 users from students, educa- tors and researchers around the globe. In the short time since the “IBM Quantum Experience” became available, a flurry of research results on 5-qubit systems have been published derived from the platform hardware [2, 3, 4, 5, 6]. Quintuple is an open-source object-oriented Python module implementing the simulation of the “IBM Quantum Experience” hardware. Quintuple quantum algorithms can be programmed and run via a custom language fully compatible with the “IBM Quantum Experience” or in pure Python. Over 40 example programs are provided with expected results, including Grover’s Algorithm and the Deutsch-Jozsa algorithm. Quintu- ple contributes to the study of 5-qubit systems and the development and debugging of quantum algorithms for deployment on the “IBM Quantum Experience” hardware. Keywords: quantum computing, 5-qubit, cloud quantum computing, IBM Quantum Experience, entanglement PROGRAM SUMMARY Manuscript Title: Quintuple: a Python 5-qubit quantum computer simulator to facilitating cloud quantum computing Authors: Christine Corbett Moran Program Title: Quintuple Licensing provisions: none Programming language: Python Computer: Any which supports Python 2.7+ Operating system: Cross-platform, any which supports Python 2.7+, e.g. -
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, -
Curriculum Vitae
Curriculum Vitae Vedran Dunjko [email protected] EDUCATION PhD in Physics 2010 { 2012 Heriot-Watt University, Edinburgh, UK. Supervisor: Prof. Dr. E. Andersson, Prof. Dr. G. S. Buller and Dr. E. Kashefi Postgraduate studies in Mathematics 2007 { 2010 University of Zagreb, Zagreb, Croatia Undergraduate and Master studies 1999 { 20071 in Mathematics and Computer Science University of Zagreb, Zagreb, Croatia ACADEMIC DEGREES PhD in Physics awarded 16 Nov 2012 Heriot-Watt University, Edinburgh, UK Master in Mathematics and Computer Science awarded 13 Sept 2007 University of Zagreb, Zagreb, Croatia (Dipl. -Ing. degree) Highest grade (5) achieved Final Exam and Graduation Dissertation EMPLOYMENT Assistant Professor (tenure track), LIACS & LION Leiden University 2018 { present Leiden, Netherlands Post-doctoral position at Max Planck Institute of Quantum Optics 2017 { 2018 Garching, Germany (Group Cirac) Post-doctoral position at Institute for Theoretical Physics 2013 { 2017 University of Innsbruck, Austria (Group Briegel) Research associate at the School of Informatics, 2012 { 2013 University of Edinburgh, UK Research assistant at the Division of Molecular Biology 2008 { 2015 Rud¯er Boˇskovi´cInstitute, Zagreb, Croatia 1My studies were protracted during the period of 1999-2008 as I had a second focus. I was a competing track and field athlete (multiple national finalist, 6-10 training sessions/week and co- coach for the horizontal and vertical jumps). In 2006 I also completed mandatory military service. My physics studies began in 2010. 1 FUNDING, FELLOWSHIPS, GRANTS NWO/NWA project \Quantum Inspire" Dec 2020 Google unrestricted Gift Jun 2020 Project \HybridQML" SurfSARA project funding Mar 2020 Project \Quantum Computing for Quantum Chemistry" EC H2020 project \NEASQC " Sept 2020 WP lead for machine learning and optimization Funding for two PhD students, 4 years Jul 2019 External industrial funding from Total Funding for post-doctoral researcher, 1 year Jul 2018 Quantum Software Consortium, internal call. -
Michael Bremner T + 44 1173315236 B [email protected] Skype: Mickbremner
Department of Computer Science University of Bristol Woodland Road BS8 1UB Bristol United Kingdom H +44 7887905572 Michael Bremner T + 44 1173315236 B [email protected] Skype: mickbremner Research Interests: Quantum simulation, computational complexity, quantum computing architectures, fault tolerance in quantum computing, and quantum control theory. Personal details Date of birth: 5th August 1978 Languages: English Nationality: Australian Marital status: Unmarried Education 2001–2005 PhD, Department of Physics, University of Queensland. Project: Characterizing entangling quantum dynamics. Supervised by Prof. Michael Nielsen and Prof. Gerard Milburn. 2000 BSc with Honours Class I, Department of Physics, University of Queensland. Project: Entanglement generation and tests of local realism in quantum optics. Supervised by Prof. Tim Ralph and Dr Bill Munro. 1997–1999 BSc (Physics), University of Queensland. 1996 Senior certificate, Villanova College, Brisbane. Completed high school education. Professional experience 2007–Present Postdoctoral researcher, Department of Computer Science, University of Bristol. Supervised by Prof. Richard Jozsa. 2005–2007 Postdoctoral researcher, Institute for Theoretical Physics and Institute for Quantum Optics and Quantum Information, University of Innsbruck. Supervised by Prof. Hans Briegel Scholarships and awards 2005 Dean’s commendation for excellence in a PhD thesis, University of Queensland. 2001–2005 Australian Postgraduate Award, Australian Research Council. 1998–1999 Scholarships for summer vacation research at the University of Queensland 1997–1999 Four Dean’s commendations for high achievement, University of Queensland. 1996 Australian Students Prize – Awarded to the top 500 students completing high school studies. 1/5 Recent conference presentations 2009 D. Shepherd (speaker) and M. J. Bremner Instantaneous Quantum Computation con- tributed talk at Quantum Information Processing 2009, Santa Fe. -
Quantum Algorithms: Equation Solving by Simulation
news & views QUANTUM ALGORITHMS Equation solving by simulation Quantum computers can outperform their classical counterparts at some tasks, but the full scope of their power is unclear. A new quantum algorithm hints at the possibility of far-reaching applications. Andrew M. Childs uantum mechanical computers have state |b〉. Then, by a well-known technique can be encoded into an instance of solving the potential to quickly perform called phase estimation5, the ability to linear equations, even with the restrictions calculations that are infeasible with produce e–iAt|b〉 is leveraged to create a required for their quantum solver to be Q –1 present technology. There are quantum quantum state |x〉 proportional to A |b〉. efficient. Therefore, either ordinary classical algorithms to simulate efficiently the (A similar approach can be applied when computers can efficiently simulate quantum dynamics of quantum systems1 and to the matrix A is non-Hermitian, or even ones — a highly unlikely proposition — or decompose integers into their prime when A is non-square.) The result is a the quantum algorithm for solving linear factors2, problems thought to be intractable solution to the system of linear equations equations performs a task that is beyond the for classical computers. But quantum encoded as the quantum state |x〉. reach of classical computation. computation is not a magic bullet — some Producing a quantum state proportional Proving ‘hardness’ results of this kind problems cannot be solved dramatically to A–1|b〉 does not, by itself, solve the task is a widely used strategy for establishing faster by quantum computers than by at hand. -
Julio Gea-Banaclochea 'Department of Physics, University of Arkansas, Fayett,Eville, Arkansas, 72701, USA
Geometric phase gate with a quantized driving field Shabnam Siddiquia arid Julio Gea-Banaclochea 'Department of Physics, University of Arkansas, Fayett,eville, Arkansas, 72701, USA ABSTRACT We have studied the performance of a geometric phase gate with a quantized driving field numerically, and developed an analytical approximation that yields some preliminary insight on the way the nl~hltbecomes entangled with the driving field. Keywords: Quantum computation, adiabatic quantum gates, geometric quantum gates 1. INTRODUCTION It was first suggested by Zanardi and ~asetti,'that the Berry phase (non-abelian holon~m~)~-~might in principle provide a novel way for implementing universal quantum computation. They showed that by encoding quantum information in one of the eigenspaces of a degenerate Harniltonian H one can in principle achieve the full quantum computational power by using holonomies only. It was then thought that since Berry's phase is a purely geometrical effect, it is resilient to certain errors and may provide a possibility for performing intrinsically fault-tolerant quantum gate operations. In a paper by Ekert et.al,5 a detailed theory behind the implementation of geometric computation was developed and an implementation of a conditional phase gate in NMR was shown by Jones et.al.6 This attracted the attention of the research community and various studies were performed to study the rob~stness~-'~of geometric gates and the implementation of these gates in other systenls such as ion-trap, solid state and Josephson qubits.lO-l4 In this paper we study an adiabatic geometric phase gate when the control system is treated as a two mode quantized coherent field. -
Blindness and Verification of Quantum Computation with One Pure Qubit
Blindness and Verification of Quantum Computation with One Pure Qubit Theodoros Kapourniotis1, Elham Kashefi1, and Animesh Datta2 1 School of Informatics, University of Edinburgh 10 Crichton Street, Edinburgh EH8 9AB, UK [email protected], [email protected] 2 Clarendon Laboratory, Department of Physics, University of Oxford, OX1 3PU, United Kingdom [email protected] Abstract While building a universal quantum computer remains challenging, devices of restricted power such as the so-called one pure qubit model have attracted considerable attention. An important step in the construction of these limited quantum computational devices is the understanding of whether the verification of the computation within these models could be also performed in the restricted scheme. Encoding via blindness (a cryptographic protocol for delegated computing) has proven successful for the verification of universal quantum computation with a restricted verifier. In this paper, we present the adaptation of this approach to the one pure qubit model, and present the first feasible scheme for the verification of delegated one pure qubit model of quantum computing. 1998 ACM Subject Classification Quantum computation theory, Cryptography, Model verifica- tion and validation Keywords and phrases Delegated Computing, Verification, Measurement-based Model Digital Object Identifier 10.4230/LIPIcs.TQC.2014.176 1 Introduction The physical realisation of quantum information processing requires the fulfilment of the five criteria collated by DiVincenzo [13]. While enormous progress had been made in realising them since, we are still some way from constructing a universal quantum computer. This raises the question whether quantum advantages in computation are possible without fulfilling one or more of DiVincenzo’s criteria. -
Annual Report 2019
Annual Report 2019 ANNUAL REPORT 2019 A synthetic diamond on a microwave transmission line illuminated by a green laser. Atom-sized crystal defects inside the diamond are used as microscopic heat machines to explore thermodynamics in the quantum regime/Jonas Becker Front cover: A micro-machined enclosure for a superconducting circuit for quantum computing, designed for scalability/Peter Spring CONTENTS Contents Foreword ................................................................................ 3 Applications & Software ................................................25 Secure Network Applications.......................................25 Introduction ......................................................................... 4 Quantum Enabled Applications ..................................25 Programme Structure ..................................................... 5 Quantum/Classical Emulation and Interfacing .......26 Achievements ................................................................... 6 NQIT Consortium ............................................................. 7 Wider Engagement ...........................................................29 UK Partners Map .............................................................. 8 Public Engagement .......................................................29 Industry and Strategic Partners ................................... 9 Responsible Research and Innovation ......................29 People .............................................................................. 10 Inter-Hub -
Quantum Machine Learning Algorithms: Read the Fine Print
Quantum Machine Learning Algorithms: Read the Fine Print Scott Aaronson For twenty years, quantum computing has been catnip to science journalists. Not only would a quantum computer harness the notorious weirdness of quantum mechanics, but it would do so for a practical purpose: namely, to solve certain problems exponentially faster than we know how to solve them with any existing computer. But there’s always been a catch, and I’m not even talking about the difficulty of building practical quantum computers. Supposing we had a quantum computer, what would we use it for? The “killer apps”—the problems for which a quantum computer would promise huge speed advantages over classical computers—have struck some people as inconveniently narrow. By using a quantum computer, one could dramatically accelerate the simulation of quantum physics and chemistry (the original application advocated by Richard Feynman in the 1980s); break almost all of the public-key cryptography currently used on the Internet (for example, by quickly factoring large numbers, with the famous Shor’s algorithm [14]); and maybe achieve a modest speedup for solving optimization problems in the infamous “NP-hard” class (but no one is sure about the last one). Alas, as interesting as that list might be, it’s hard to argue that it would transform civilization in anything like the way classical computing did in the previous century. Recently, however, a new family of quantum algorithms has come along to challenge this relatively- narrow view of what a quantum computer would be useful for. Not only do these new algorithms promise exponential speedups over classical algorithms, but they do so for eminently-practical problems, involving machine learning, clustering, classification, and finding patterns in huge amounts of data.