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A predicting platform for designing semiconductor quantum devices

A predicting platform for designing semiconductor quantum devices


Quantum technologies and Artificial Intelligence are currently leading R&D and industrial investments of Europe due to their potential to bring large transformations in the digital economy and manufacturing of EU. This project will be based in the research and technology ecosystem of Grenoble, in France, hosted in a Joint Research Unit of Université Grenoble Alpes and CEA. The aim of the project will be to develop a computer-assisted model able to predict the modes of operation of a silicon quantum transistor, giving access to accurate predictions that can decrease the costs involved in designing a quantum circuit. Machine Learning algorithms will be employed for further decreasing the time it takes to optimize the design in a fraction of the original. This project is expected to be an invaluable addition to the enabling technologies for the EU research and innovation and its goals for 'quantum digitization'.
The work will be undertaken by Dr. Eleni Chatzikyriakou, with an interdisciplinary background and expertise in the development of computational methods for electron device operation and will be supervised by Dr. Xavier Waintal, that has twenty years of experience in computational studies of various physical phenomena in the quantum regime, and is the leading coordinator of the open source software for quantum transport Kwant.
The fellow will be part of a highly specialized group in quantum technologies and will have the opportunity to significantly broaden her knowledge in both quantum technology and Machine Learning through the training and implementation of the action. It is therefore expected that, together with the training in leading and management skills, by the end of the fellowship, she will be in a position to take on leading positions and coordinate interdisciplinary actions by taking advantage of her own and the new networking opportunities that will arise in the fields of (Opto)Electronic Engineering, Computer Science and Physics.
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Activity type

Higher or Secondary Education Establishments

EU Contribution

€ 184 707,84

Project information

Grant agreement ID: 840550


Grant agreement signed

  • Start date

    1 September 2019

  • End date

    31 August 2021

Funded under:


  • Overall budget:

    € 184 707,84

  • EU contribution

    € 184 707,84

Coordinated by: