Manipulating information at the quantum level is profoundly different from manipulating it classically. This opens the door to some new and surprising applications such as quantum cryptography (in which the security of the encryption of a message is guaranteed by the laws of physics) or quantum computers (which could solve certain problems such as factoring exponentially faster than classical computers). The Laboratoire d’Information quantique is actively working on different aspects quantum information, both at the theoretical and experimental level.

Dynamical Systems and Brain Inspired Computing - Description |

Workshop supported by the International Solvay Insitutes.
ULB (Université Libre de Bruxelles) - Campus Plaine - Bld de la Plaine - 1050 Brussels For a map of the campus see http://www.solvayinstitutes.be/pdf/campus.pdf
I. Fischer (Universitat de les Illes Baleares), Julie Grolier (Thales), Herbert Jaeger (Jacobs University), Juan-Pablo. Ortega (University of St. Gallen, Switzerland), P. Bienstman (Ghent University), C. van den Broek (Universiteit Hasselt), J. Dambre (Ghent University), J. Danckaert (Vrije Universiteit Brussel), M. Haelterman (Université libre de Bruxelles), R. Lambiotte (Université de Namur), S. Massar (Université libre de Bruxelles), G. Van der Sande (Vrije Universiteit Brussel), Jean-Pierre Locquet (KUL)
P. Bienstman (Ghent University), C. van den Broek (Universiteit Hasselt), J. Dambre (Ghent University), J. Danckaert (Vrije Universiteit Brussel), M. Haelterman (Université libre de Bruxelles), R. Lambiotte (Université de Namur), S. Massar (Université libre de Bruxelles), G. Van der Sande (Vrije Universiteit Brussel), Jean-Pierre Locquet (KUL)
-Ingo Fischer (Universitat de les Illes Baleares) -Claudio Mirasso (Universitat de les Illes Baleares) -Julie Grolier (Thales) -Herbert Jaeger (Jacobs University) -Daniel Brunner (Université de Franche Comté) -Thomas Van Vaerenbergh (HP Labs) -Vijay Balasubramanian (Penn State Univ. & VUB) -Julien Sylvestre (Sherbrook University) -Christof Teuscher (Portland State University) -Antonio Politi (Aberdeen) -Wolf Singer (Ernst Strüngmann Institute (ESI) for Neuroscience) -David Wolpert (Santa–Fe institute) -Robert Legenstein (Graz University of Technology) -Helmut Hauser (University of Bristol) -Raul Vicente (University of Tartu) -Leslie Valiant (Harvard) - to be confirmed
Although computer science and physics are two distinct fields, they are intimately linked. “Information is physical” (R. Landauer) and must be processed by physical devices. This fact is behind such far reaching conclusions as the Church-Turing thesis (the statement that all classical computers have equivalent computational power), the resolution of Maxwell’s paradox, the introduction of quantum computing, or the goal of understanding how information is processed in biological neural networks. Over the past few years several artificial intelligence algorithms have been introduced, including “reservoir computing” and “deep learning”, which provide another such connection. At the heart of these algorithms, and in particular the reservoir computing algorithm, are dynamical systems of the type studied by physicists interested in complex systems, composed of many internal variables that exhibit a transition from stability to deterministic chaos. Reservoir computing provides state of the art performance for tasks that are generally deemed hard, such as speech recognition or time series prediction. It also provides a new connection between physics, computer science and engineering. From the physics point of view, reservoir computing raises new and fascinating questions: What is the computational power of nonlinear dynamical systems? How can one characterize their computational power? Can the powerful tools of theoretical physics and information theory be brought to bear on this question? From the point of view of engineering, other fascinating question arises: Can the flexibility of the reservoir computing paradigm be used to devise new, unconventional, architectures for computing machines, based for instance on optics or unconventional electronics components? It also raises interesting connections with other areas, such as neurosciences (are these algorithms relevant to how the brain processes information?), or robotics (how to control soft compliant robots?). Some exciting results at the interface of these fields have appeared during the past few years, including the experimental realization of reservoirs with performance, not only in error on a given task, but also in speed, comparable to the best digital implementations. The aim of this workshop is to bring together researchers working at the interface between physics, computer science, engineering, neuroscience, in order to foster the development of novel computing architectures inspired by artificial intelligence algorithms and by how the brain processes information. Questions of interest include: - Novel/improved methods for using recurrent dynamical systems for information processing, - Theoretical tools for understanding the information processing capability of recurrent dynamical systems, - Experimental implementations of such systems, including optical and (unconventional) electronic implementations. - Connections to other areas, including neurosciences, soft robotics, etc. |