Description: Ellis Fellows QPhML
Increased computational power and data availability, as well as algorithmic advances, have led to impressive machine learning applications in many areas such as computer vision, pattern recognition, robotics and AI. At the same time, conventional CMOS technology is reaching its physical limits and the energy consumption of computing is reaching alarming proportions. There is therefore a great need to design novel computing paradigms that face these challenges.
The aim of the Ellis program Quantum and Physics based machine learning (QPhML) is to use concepts from quantum physics and statistical physics to develop novel machine learning algorithms with the ultimate aim to realize novel future, possibly energy efficient, hardware implementations.
The program is part of the recent European initiative called ELLIS (European Laboratory for Learning and Intelligent Systems) to stimulate research on machine learning by building networks of top reseach groups in Europe.