Project description
Machine learning advancing quantum science and technology
Machine learning (ML) holds great potential in quantum science. Supported by the Marie Skłodowska-Curie Actions programme, the ML4Q project explores how ideas and techniques from ML and quantum can feed into and benefit each other. It provides interdisciplinary training to 10 doctoral researchers in extending quantum and classical ML-based prediction of materials and matter properties to strongly-correlated regimes, and further developing quantum technologies through ML, thus enabling new approaches to solve problems out of reach of classical computers. This approach can address some of the world’s most pressing challenges, such as developing tools for discovering more environmentally friendly chemical processes and efficient materials, and accelerating the development of quantum technologies, thus giving Europe an edge in the global tech race.
Objective
"The Marie Skłodowska-Curie Doctoral Network ""ML4Q - Machine Learning for Quantum"" provides high-level interdisciplinary, intersectoral and international training to 10 doctoral researchers who will explore how machine learning and quantum science technology can be combined to (i) extend quantum and classical machine learning based prediction of materials and matter properties and to strongly-correlated regimes, and (ii) accelerate the development of quantum technologies through machine learning, thus enabling new approaches to solving outstanding problems currently out of reach of classical computers. This has the potential to address some of the world's most pressing challenges, such as developing tools for discovering more environmentally friendly chemical processes and efficient materials, or accelerating the development of quantum technologies which will give Europe an edge in the global tech race. ML4Q fellows will realize this vision will through their individual projects and interdisciplinary collaborations reinforced by a comprehensive training program which combines cutting-edge research with a focus on networking, career development for academic and non-academic career paths, open science and responsible research and innovation for society, that will enable them to shape emerging technologies and the next digital transformation in Europe. The consortium consists of 5 academic and 5 non-academic research partners (including 2 leading Eu QT startups) and 11 principal investigators who bring together all the necessary expertise computer science, AI and machine learning, quantum technology, and chemistry and materials science, as well as their interfaces. Together we will prepare the next generation of strong, resilient, flexible, and creative quantum and computer scientists with the combination of skills needed to meet the future needs of the rapidly evolving innovative materials, quantum technologies industries, as well as other knowledge based sectors."
Fields of science (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- natural sciences physical sciences quantum physics
- natural sciences computer and information sciences artificial intelligence machine learning
You need to log in or register to use this function
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
-
HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
MAIN PROGRAMME
See all projects funded under this programme
Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-DN - HORIZON TMA MSCA Doctoral Networks
See all projects funded under this funding scheme
Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) HORIZON-MSCA-2022-DN-01
See all projects funded under this callCoordinator
Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
67081 Strasbourg
France
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.