Quantum Technology

Digital computers are visibly approaching their physical limits. Thus, there is a growing interest in post-digital computing approaches. This covers quantum information processing (QIP) and artificial neural networks (ANN) implemented in specialized hardware. Recent years have seen tremendous progress in both fields, both in theory and in experiments. The first QIP systems have demonstrated advantages compared to classical supercomputers for specific tasks, while ANNs showed capability for machine learning (ML), e.g. in pattern recognition. Research towards the realization of both is motivated by the unique opportunities across a range of intellectual and technical frontiers. Speech recognition, home automation, and autonomous driving are typical problems for ML, while QIP is expected to change our notions of physics, combinatorial problems, and possibly even biological systems. The common goal of both is to solve a variety of computational tasks that are inefficient to solve on digital computers.

Optical platforms have high potential for both QIP and ANNs. The first components for optical QIP have been realized, e.g. small processors, highly efficient non-classical light sources, superconducting single photon detectors. Similar components enabled chip-integrated optical artificial neural networks (ONNs), which are in principle composed of linear optical networks and non-linearities. However, putting together all required components to demonstrate large scale photonic computers that outperform today’s digital hardware remains challenging: given the variety and complexity of experimental and theoretical approaches, it is nearly impossible to unite expertise from all involved fields within one single research team. This seminar will bring together experts from different communities in order to discuss the prospects and challenges of combining their fields within a joint research effort on photonics for post-digital computers. The involved discussions will be highly beneficial not only for advancing the field, but also for supporting early stage researchers in their progress on becoming experts themselves.

Invited Speakers
Ulrik L. Andersen, Technical University of Denmark, Lyngby, Denmark
Stefanie Barz, University of Stuttgart, Germany
Christoph Becher, Saarland University, Germany
Daniel Brunner, FEMTO-ST, France
Hui Cao, Yale University, USA
Vedran Dunjko, Leiden University, The Netherlands
Jens Eisert, Freie Universität Berlin, Germany
Elham Kashefi, Sorbonne Université, France
Michael Kues, Leibniz Universität Hannover, Germany
Anthony Laing, University of Bristol, United Kingdom
Kai Müller, Technical University of Munich, Germany
Wolfram Pernice, University of Münster , Germany
Heike Riel, IBM Research, Switzerland
Pascale Senellart, Université Paris-Saclay, France
Christine Silberhorn, Paderborn University, Germany
Alexander Szameit, University of Rostock, Germany