Workshop on Quantum Computing and Quantum Sensors (Aug 2020)

11 Aug 2020, 15:29 → 18 Aug 2020, 18:30 Europe/Berlin

ZOOM

Zoom coordinates using the DESY System: 
https://desy.zoom.us/j/99616528733

Meeting ID: 996 1652 8733
Meeting Password: 733220
 

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

General Schedule General_Schedule-3.jpg

Instructions for the Hands-On Exercise Instructions_for_the_hands-on_exercise.docx Instructions_for_the_hands-on_exercise.pdf

Poster PAcolloquium_QT_at_DESY_11-18-Aug2020_v2.pdf

Tuesday 11 August

15:29 → 15:30 Session Chair: Karl Jansen

15:30 → 15:55 Quantum Technologies at DESY - Brief Introduction

Speaker: Kerstin Borras (DESY)

Slides 2020-08-11-WS-QT-at-DESY-v1.pdf 2020-08-11-WS-QT-at-DESY-v1.pptx

15:55 → 16:40 Quantum-Inspired Optimization based on Digital Annealer

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. 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 Andreas Rohnfelder (Fujitsu), Dr Sebastian Engel (Fujitsu)

Slides Desy_Workshop_-_Digital_Annealer.pdf

16:40 → 16:49 Break

16:49 → 16:50 Session Chair: Volker Gülzow

16:50 → 17:20 Introduction to Quantum Computing (Martin Savage, INT Washington)

Slides Savage_DESY_August2020_v1p0_pdf.pdf

17:30 → 18:00 Introduction to Error Mitigation (Lena Funcke, Perimeter Institute)

Abstract Quantum computers have the potential to outperform classical computers in a variety of tasks ranging from combinatorial optimization to machine learning to intrinsically evading the sign problem. However, current intermediate-scale quantum devices still suffer from a considerable level of noise. This talk will introduce different sources of noise and their mitigation techniques, with a particular focus on the final qubit measurement. While measurement error mitigation techniques are usually only applicable to a small number of qubits, the talk presents a novel method that can be applied to any operator, any number of qubits, and any realistic bit-flip probability. The experimental realization of the method is demonstrated on IBM quantum hardware, reducing the final measurement error by up to one order of magnitude.

Slides Presentation_Funcke.pdf

18:10 → 18:40 Discussion (Moderator: Steven Worm)

Tuesday 18 August

13:29 → 13:30 Session Chair: Kerstin Borras

13:30 → 14:45 Hands-On Exercise (Stefan Kühn, Cyprus Institute)

Abstract During recent years various frameworks for developing, testing and running quantum programs on (prototype) hardware have emerged. In this hands-on tutorial we will take a closer look at a particular one, the Python based Qiskit SDK. I will provide a very basic introduction before we proceed with a hands-on session, in which we try to implement simple quantum programs and algorithms, and to visualize the results. Detailed instructions and Jupyter notebooks for the tutorial can be found here at https://github.com/kuehnste/QiskitTutorial

Slides DESY_Qiskit_Intro_Kuehn.pdf

14:50 → 14:59 Break

14:59 → 15:00 Session Chair: Dirk Krücker

15:00 → 15:30 Machine Learning with Quantum Computers (Maria Schuld, Xanadu, Uni KwaZulu-Natal)

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".

Slides qml_maria.pdf

15:40 → 16:10 Quantum Technologies at CERN (Alberto DiMeglio, Sofia Vallecorsa - CERN Openlab)

Abstract The CERN Quantum Technology Initiative: Overall Objectives and Quantum Computing Projects In 2018 CERN openlab organized one of the first Quantum Computing in HEP workshops, an event that was rather surprisingly attended by more than 400 people across research and industry. Since then a number of pilot investigation projects have been set up to assess the areas where quantum computing and quantum machine learning could provide benefits. Based on this initial investigations, CERN has recently decided to set up a dedicated R&D initiative to explore the broad potential and the requirements of quantum technologies in HEP and establish a programme of collaborations with other national and international quantum technology initiatives. This talk highlights the main objectives of the CERN Quantum Technology Initiative across the four main areas of computing, sensing, networks and information theory, and specifically showcases the ongoing projects in quantum computing applied to different HEP workloads.

Slides QC@CERNopenlab_DESY_Aug2020.pdf

16:20 → 16:29 Break

16:29 → 16:30 Session Chair: Steven Worm

16:30 → 17:00 Table-top experiments for physics beyond the Standard Model: A theory perspective (Asimina Arvanitaki, Perimeter Institute)

Abstract In the last decade we have seen a wide array of new experimental advances. I will briefly describe well-motivated phenomena beyond the Standard Model that these advances can probe with a focus on Dark Matter.

Slides DESYAug2020.pdf

17:10 → 17:40 New and refurbished techniques to search for dark matter (Dmitry Budker, Helmholtz Institute Mainz, JGU)

Abstract Ultralight bosonic dark fields and axion quark nuggets are two promising and completely different candidate dark-matter frameworks. We will discuss examples of ongoing experiments searching for such dark matter using a variety of technologies: from nuclear magnetic resonance spectrometers to networks of optically pumped magnetometers to superconducting gravimeters, seismometers and Doppler-free laser spectrometers, to atomic clocks and atom interferometers. In the coming years, there will be nowhere for dark matter to hide.

Slides DESY_quantum_Aug_2020.pptx

17:50 → 18:20 Discussion (Moderator: Cigdem Issever)