Workshop on Quantum Computing and Its Application
A one-day workshop on Quantum Computing and its applications in drug development will take place onThursday March 16, 2017 at George Washington University (Foggy Bottom campus), Washington, DC, following ENAR 2017 Spring meeting (March 12-15 in Washington, DC). The workshop is co-organized by GWU Department of Statistics, Lockheed Martin Corporation and ICON Plc.
The inception of first quantum computers and rapid evolution of quantum algorithms has attracted a considerable interest in a number of diverse areas, including cryptography, predictive analytics, financial engineering, and machine learning. Nowadays statisticians are often involved in projects that require exploring, understanding and interpreting massive multivariate data. Genomics, clinical trials and computerized healthcare monitoring systems are popular examples that generate that type of data in the hope that their analysis may increase inferential and predictive power. The respective computations can be prohibitively slow in the classical computing paradigm. Quantum computers provide a computational power many times the order of existing computers which is critical for training machine learning algorithms, such as quantum support vector machines, to predict clinical trial outcomes from genomic and phenotypic information. Other optimization problems that arise in statistical applications (e.g., simulated annealing algorithms in optimal model-based experimental design) may benefit from developments in quantum computing.
The workshop will bring together researchers in the field of statistical applications of quantum computing in health care and drug development, and will feature overview presentations on quantum computing, talks on quantum algorithms and its links with statistics, as well as case studies and round table discussions.
Location: Duques Hall, Room 151, 2201 G St. NW, Washington, DC 20052
Steve Adachi, Lockheed Martin
Title: An Overview of Quantum Computing Concepts and Applications in Healthcare
With the ever-increasing demand for computing horsepower challenging the ability of conventional High Performance Computing to keep pace, Lockheed Martin has made significant investments in quantum computing as a potentially revolutionary and complementary computational paradigm. LM’s acquisition in 2011 of a D-Wave quantum annealing machine, jointly operated by LM and the University of Southern California, has enabled the research community to gain a better understanding of what the D-Wave machine is doing, and catalyzed progress in the development of quantum computing hardware and algorithms. Through our 6 years of experience developing applications on actual quantum hardware, we have demonstrated the potential for quantum annealing to address hard computational problems in the aerospace industry, as well as other industries including healthcare. We will briefly review the concepts behind quantum computing, and describe how this technology could be applied to solving optimization and machine learning problems in the healthcare industry.
Steve Adachi is an LM Fellow at the Advanced Technology Center of Lockheed Martin Space Systems Company. In his research he has investigated how quantum annealing could be applied to machine learning and optimization problems in the aerospace industry, as well as other industries including healthcare. Prior to his current role, he worked on several large satellite programs at LM, and also held leadership positions at AT&T Bell Labs/Lucent Technologies and Covad Communications. He has a B.S. in Mathematics from Harvey Mudd College and a Ph.D. in Applied Mathematics from Brown University.
Alexei Ashikhmin, Bell Labs
Title: Introduction into Quantum Computations
In this talk I will present basic notions of quantum computations. In particular, I will discuss postulates of quantum state, unitary evolution, and quantum measurement. Modern description of these postulates is simple and short. It does not require any knowledge of quantum mechanics and uses only simple concepts of linear algebra and complex numbers. Next, I will outline quantum Fourier transform, which is exponentially faster than its classical counterpart. Finally, I will briefly talk about my research on quantum error correcting codes.
Dr. Alexei Ashikhmin is a Distinguished Member of Technical Staff in the Communications and Statistical Sciences Research Department of Bell Labs, Murray Hill, New Jersey. His research interests include classical and quantum information theory, classical and quantum correcting codes, and communication theory. Dr. Ashikhmin is an IEEE fellow, and a recipient of 2014 Thomas Edison Patent Award and 2004 the Stephen O. Rice Prize for the best paper of IEEE Transactions on Communications. In 2002, 2010, and 2011, he received Bell Laboratories President Awards for breakthrough research in information and communication theory.
From 2003 to 2006, and 2011 to 2014 Dr. Ashikhmin served as an Associate Editor for IEEE Transactions on Information Theory. Dr. Ashikhmin is an adjunct professor of Columbia University and NYU. He teaches courses on Quantum Computations and Communications, Error Correcting Codes, and Massive MIMO Wireless Systems.
Ali Eskandarian, George Washington University
Title: Quantum Mysteries and the Logic of Computation
The notions of probability and statistics play important but fundamentally different roles in classical and modern theories of physics. The less intuitive aspects of quantum theory lead to a deeper understanding of how nature operates. What appears to be mysterious according to classical laws of nature, has important consequences in how we control and guide physical processes in tasks related to computation and communication. Here, we attempt to elucidate the way quantum concepts are understood and employed by scientists, and the way they reshape our thinking about related fields. The emphasis will be on foundational issues.
Ali Eskandarian is Dean of GW’s Virginia Science and Technology Campus and the College of Professional Studies. His research interests in physics have been in nuclear few-body systems, astrophysics, foundations of quantum theory, and quantum computation/information. He is a founding member and co-director of GW’s Center for Quantum Computation, Information, Logic, and Topology.
Valerii Fedorov, ICON Clinical Research
Title: Numerical Statistics and Quantum Algorithms
The major objective of the talk is to discuss opportunities of quantum computing in statistics and related areas. Quantum computing hardware and its algorithmic support are rapidly improving, we see more and more applications of quantum information processing, machine learning and quantum Monte-Carlo techniques. Machine learning and artificial intelligence comprise important applied areas where quantum optimization (for instance, quantum annealing) is expected to give a major boost. Classification problems constitute another area where quantum computing looks promising.
Professor Valerii Fedorov joined ICON plc in 2015 as VP, Innovation Center. Prior to ICON Plc, he was VP and the Head of the Predictive Analytics, Innovation, Quintiles, NC. Before that for 12 years he led the Research Statistics Unit at GlaxoSmithKline Inc., worked as the Senior Research Statistician at the Oak Ridge National Laboratory and lectured as a Visiting Professor of Statistics at the University of Minnesota. Before his USA career Professor Fedorov served as the Head of the Department of Mathematical Statistics in the Central Institute of Mathematical Economics of the Russian Academy of Sciences in Moscow, Russia. He lectured and conducted research as a Visiting Professor or as a Visiting Scholar at the Isaac Newton Institute for Mathematical Sciences, Cambridge; the Imperial College in London and the City University of London, UK; Free University and Humboldt University in Berlin, Germany; Vienna University and University of Economics in Austria. He worked for five years at the International Institute of Applied System Analysis in Vienna, Austria.
Professor Fedorov had earned his Ph.D. in Mathematics and Physics from the Moscow State University. He also holds a D.Sc. in Statistics from the Russian Academy of Sciences. Valerii Fedorov is an author of more than 200 publications including several books in various areas of statistics and biostatistics, such as design of clinical trials, bioequivalence, random effects models, regression analysis, numerical methods in design of experiments, econometrics, theory of optimal design of experiments, model-oriented adaptive design of experiments, and Bayesian methods in experimental design. His monograph Theory of Optimal Experiments, Academic Press, is the most cited book in optimal experimental design area. Professor Fedorov is an ASA Fellow, Honorary Professor of Cardiff University, UK, and Adjunct Scholar of University of Pennsylvania, USA, elected member and former Council Member of the International Statistical Institute.
Andrew Rukhin, National Institute of Standards and Technology (NIST)
Title: Randomness Testing: Physical Generators and Statistical Procedures
In this talk I examine some statistical issues arising when testing for randomness the output of a random number generator. The suite of such tests developed at the National Institute of Standards and Technology is discussed and a quality control chart for the output of a quantum random number generator is reviewed.
After emigrating from the USSR, Andrew Rukhin taught at Purdue University (1977-1987), at University of Massachusetts, Amherst (1987-1989), and University of Maryland at Baltimore County(1987-2008). In 1994 he was appointed Mathematical Statistician in the Statistical Engineering Division, the National Institute of Standards and Technology where he works now. He is engaged in applied statistical research in Interlaboratory Studies, Statistical Decision Theory, Information Theory, Testing of Randomness, Bayesian Statistics, Change-Point Problems, Classification and Discrimination, Adaptive and Recursive Procedures, and Meta-Analysis.
Andrew is a Fellow of the Institute of Mathematical Statistics, and a Fellow of the American Statistical Association. He won the Senior Distinguished Scientist Award By Alexander von Humboldt-Foundation (1990) and the Youden Prize for Interlaboratory Studies (1998, 2007). He is an Associate Editor of Journal of Statistical Planning and Inference, and of Mathematical Methods of Statistics.
Yazhen Wang, Department of Statistics, University of Wisconsin-Madison
Title: Statistics and Quantum Computing
Quantum computation and quantum information are of great current interest in fields such as computer science, physics, engineering, chemistry and mathematical sciences. They will likely lead to a new wave of technological innovations in communication, computation and cryptography. This talk will first give a brief introduction on quantum computation and quantum information and then present my recent work on statistics and quantum computing.
Dr. Yazhen Wang is Professor and Chair of Department of Statistics, University of Wisconsin-Madison. He obtained his Ph.D in statistics from University of California at Berkeley in 1992. He is the Fellow of ASA and IMS. His research areas include financial econometrics, quantum computation, quantum simulation, ultra-high dimensional inference, nonparametric curve estimation, wavelets and multi-scale methods, change points, long-memory processes, and order restricted inference. He has served as NSF program director, various committees of ASA, IMS and ICSA, Editor and/or Associate Editor of Annals of Statistics, Annals of Applied Statistics, Journal of the American Statistical Association, Statistica Sinica, The Econometrics Journal, andStatistics and Its Interface.