Workshop on Quantum Computing and Quantum Sensors (Aug 2020)

from to (Europe/Berlin)
at ZOOM
Description

The timetable of the two workshop days is under construction.
Please check before the meetings.

The zoom coordinates will be posted as soon as the schedule is finalized.

The instructions for the hands-on exercise are posted here.

Material:
Go to day
  • Tuesday, August 11, 2020
    • 15:29 - 15:30 Session Chair: Karl Jansen 1'
    • 15:30 - 15:55 Quantum Technologies at DESY - Brief Introduction 25'
      Speaker: Kerstin Borras (DESY)
    • 15:55 - 16:30 Quantum-Inspired Optimization based on Digital Annealer 35'
      Abstract: 
      Combinatorial optimization problems thus finding a (global) optimum in a huge search space are one of the most challenging problems in today's industry. Classical computer architectures are typically limited as the problem size increases, Quantum Computers are not there yet to solve real world problems. Â The presentation will outline combinatorial optimization cases in various industries, provide insights on Digital Annealer, a bridge technology to tackle these problems already today, and demonstrate how to represent the problem in a mathematical equation solvable by Digital and Quantum Annealers. 
      Speakers: Dr. Sebastian Engel (Fujitsu), Dr. Andreas Rohnfelder (Fujitsu)
    • 16:40 - 16:49 Break
    • 16:49 - 16:50 Session Chair: Volker Gülzow 1'
    • 16:50 - 17:20 Introduction to Quantum Computing (Martin Savage, INT Washington) 30'
    • 17:30 - 18:00 Introduction to Error Mitigation (Lena Funcke, Perimeter Institute) 30'
    • 18:10 - 18:40 Discussion (Moderator Cigdem Issever)
  • Tuesday, August 18, 2020
    • 13:30 - 14:45 Hands-On Exercise (Stefan Kühn, Cyprus Institute) 1h15'
    • 14:50 - 14:59 Break
    • 14:59 - 15:00 Session Chair: Dirk Krücker 1'
    • 15:00 - 15:30 Machichine Learning with Quantum Computers (Maria Schuld, Xanadu, Uni KwaZulu-Natal) 30'
      Abstract
      A popular approach to machine learning with quantum computers is to interpret the quantum device as a machine learning model that loads input data and produces predictions. By optimizing the quantum circuit, the "quantum model" can be trained like a neural network. This talk highlights different aspects of such "variational quantum machine learning algorithms", including their role in the development of near-term quantum technologies, their close links to kernel methods, and how to get gradients of quantum computations. The practical integration of quantum circuits with machine learning libraries such as PyTorch and Tensorflow is illustrated with the open-source software framework "PennyLane".
    • 15:40 - 16:10 Quantum Technologies at CERN (Alberto DiMeglio - CERN Openlab) 30'
    • 16:20 - 16:29 Break
    • 16:29 - 16:30 Session Chair: Steven Worm 1'
    • 16:30 - 17:00 Quantum Sensors (Theory) (Asimina Arvanitaki, Perimeter Institute) 30'
    • 17:10 - 17:40 Quantum Senors (Experiment) (Dmitry Budker, Helmholtz Institute Mainz, JGU) 30'
    • 17:50 - 18:20 Discussion