Quantum Technologies Initiative

In the past few years, we have been witnessing the second quantum revolution, and there has been enormous efforts in Europe and internationally on creating new centres of excellence on Quantum Technologies. These include substantial government funding such as of EU "Quantum Technologies Flagship”, which "aims to put Europe at the forefront of the second quantum revolution", EPSRC UK "Quantum Technologies Theme", and the US "National Quantum Initiative".

The central task of this community is to establish the "EUTOPIA Quantum Technologies Initiative", by capitalising on the existing scientific and teaching cooperation between a number of EUTOPIA partners and to bring this cooperation to a new level by creating the core of the Quantum Technologies programme. This Centre of Excellence will be used to establish new cooperation with other partners within EUTOPIA, and other Universities/industry.

For more information, please see HERELink opens in a new window.

Machine (Deep) Learning for physicists (EUTOPIA)Link opens in a new window

This short course is open to postgraduate students of the EUTOPIA Alliance and all interested students of the Midlands Physics Alliance Graduate School. Students can received a EUTOPIA micro-credential upon completion of the course.

Machine learning and deep learning are statistical analysis techniques that use strategies of artificial intelligence to characterize complex data and extract deep information. In recent years, these techniques have begun to be used not only in traditional computer science test cases, but also in real world applications as well as, more recently, in areas of advanced physics. In condensed matter systems, such techniques have been shown to give useful insight into Ising and spin ice models [1], low dimensional topological systems [2], strongly correlated systems [3], as well as random two- and three-dimensional topological and non-topological systems [4]. It is probably fair to say that machine and deep learning will become standard statistical analysis tools in the future [5].

In this short course, we want to understand some of the basic principles underlying the recent successes of the DL approach to data analysis. We will do so in a hands-on manner, following up our theoretical understanding with examples using state-of-the-art DL packages such as Keras, FastAI, TensorFlow and PyTorch. In doing so, we will use Jupyter notebooks as frontend to Python.