TRIUMF-Helmholtz Workshop on Scientific Computing
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Europe/Berlin
DESY
DESY
Notkestr. 85, D-22607 Hamburg, Germany
Description
Scientific computing is a critical component of much of the work that takes place in particle physics at related subjects. In recent years, the amount of data produced across the globe has increased exponentially at research facilities and private businesses alike. With rapid advances in large-scale computing, big data, machine learning, and quantum computing, these technologies are beginning to have serious implications on how we do our work, and it is imperative that we remain part of this fast-changing field.
The Helmholtz Association is partnering with TRIUMF, Canadian and German universities and selected businesses to host a second workshop on selected topics in scientific computing at DESY in Hamburg, on 16/17 September 2019, to develop further collaboration and to explore new tools in scientific computing.
Note that remote connections to all rooms are provided. You can find the relevant connection details for each session in the detailed description of each session (click on the session and select "Session details") or by clicking on the small folder icon in the top-right corner of each session.
Questions and support
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Plenary session 1: Welcome and keynotes Main auditorium
Main auditorium
DESY
- 1
- 2
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10:20
Coffee break
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3
Keynote: Perspectives on the Future of Data Intensive ComputingKeynote: working group on data analyticsSpeakers: Florin Manaila (IBM), Oliver Oberst (IBM)
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4
Keynote: Building the Bridge to Exascale: Applications and Opportunities for Nuclear PhysicsSpeaker: Jack Wells
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12:30
Lunch break Canteen
Canteen
DESY
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Data Analytics Seminar room 3
Seminar room 3
DESY
- 6
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7
The Helmholtz Analytics Toolkit (HeAT), a Distributed Data Analysis FrameworkSpeaker: Daniel Coquelin (FZJ)
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- 9
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16:10
Coffe Break
- 10
- 11
- 12
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Large-scale computing Seminar room 4a
Seminar room 4a
DESY
- 13
- 14
- 15
- 16
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16:10
Coffee Break
- 17
- 18
- 19
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Quantum Computing Seminar room 1
Seminar room 1
DESY
Notkestr. 85, D-22607 Hamburg, Germany-
20
Reports from the Labs
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a) A lattice gauge theory testbed for quantum computingA simplified model, based on U(1) lattice gauge theory in 2+1 dimensions, for use as a testbed for quantum computation is constructed. Focusing on the spectrum of low-lying states, energy calculations using the variational quantum eigensolver are implemented. Some results of running experiments with this model on IBM Q devices are presented.Speaker: Richard Woloshyn (TRIUMF)
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b) Quantum Computing at DESYWe describe ongoing and possible applications of quantum computing at DESY in the areas of theoretical and experimental particle physics, astroparticle physics and photon science. A detailled example of a variational quantum computation on a superconducting qubit platform at Rigetti is presented using a hydrogen atom.Speaker: Dr Karl Jansen (NIC, DESY)
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c) Quantum Heuristic Algorithms for Hard Planning Problems from Aerospace ResearchAbstract: Quantum heuristic algorithms do not have a proven advantage over classical algorithms. However, there are indications that these approaches might outperform classical approaches for certain applications. Moreover, they are believed to work without quantum error correction and are therefore amenable to early quantum computing devices.Hard combinatorial optimization problems as they occur in logistics or traffic management are highly relevant for society and business. Even minor improvements in the solution quality can have a enormous impact in terms of costs. We present our work on mapping and solving hard real world planning problems from aerospace research with quantum heuristic algorithms like the Quantum Approximate Optimization Algorithm (QAOA) and quantum annealing (QA). In particular, we discuss the choice of representative but small problem instances as well as the mapping of the original problem to a form compatible with the device and algorithm at hand. The latter includes various obstacles like the handling of constraints, the choice of algorithm parameters and compiling.Speaker: Dr Thorge Müller (DLR)
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d) Quantum Variational Autoencoders and their applicationsGenerative models are among the most promising approaches toward understanding unlabelled data. They have a wide range of applications in structured prediction, molecular & material design, image analysis, speech synthesis, and computer vision. They pair with supervised learning models to help perform ML tasks when labeling data is expensive or labels are only available in a different domain. Quantum Boltzmann machine is a powerful generative model that can naturally be implemented on a quantum annealing device. However, the development of quantum-classical hybrid (QCH) algorithms is critical to deploy state-of-the-art computational models on current commercially available devices. A Quantum Variational Autoencoder (QVAE) is one such hybrid algorithm that consists of a latent generative process, formalized as a quantum or classical Boltzmann machine (QBM or BM). A quantum annealing processor is used for sampling from the Boltzmann prior distribution. The classical autoencoding structure is realized by a deep neural network, which allows inference to, and generating samples from, the latent space. We have successfully employed D-Wave quantum annealers as Boltzmann samplers to train end-to-end QVAE. The hybrid structure of QVAE allows us to deploy current quantum annealing devices in a QCH generative model with latent variables that achieves competitive performance on datasets such as MNIST.Speaker: Hossein Sadeghi (D-Wave)
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16:10
Coffee break
- 21
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22
Discussion on common items
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20
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18:30
Workshop dinner Bistro / Canteen
Bistro / Canteen
DESY
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Plenary session 3 Main auditorium
Main auditorium
DESY
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23
Keynote: How to know, where to look - prioritising in computer visionSpeaker: Simone Frintrop (UHH)
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24
Keynote: quantum computing in ion trapsSpeaker: Ferdinand Schmidt-Kaler (FZJ)
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10:20
Coffee break
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25
Keynote: working group one large-scale computingSpeaker: Tiziana Ferrari
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- 27
- 28
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23
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12:25
Lunch break Canteen
Canteen
DESY
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