{"id":35629,"date":"2025-07-16T11:10:09","date_gmt":"2025-07-16T09:10:09","guid":{"rendered":"https:\/\/www.risc-software.at\/fwf-funding-for-quantum-research-project-quditml\/"},"modified":"2026-06-24T17:06:36","modified_gmt":"2026-06-24T15:06:36","slug":"fwf-funding-for-quantum-research-project-quditml","status":"publish","type":"post","link":"https:\/\/www.risc-software.at\/en\/fwf-funding-for-quantum-research-project-quditml\/","title":{"rendered":"FWF funding for quantum research project “QuditML”"},"content":{"rendered":"\n
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Hagenberg, July 2025 – RISC Software GmbH is launching QuditML, a groundbreaking research project that pushes the boundaries of quantum machine learning – funded by the FWF as part of the “1000 Ideas” call.<\/em><\/p>\n<\/div>\n\n\n\n

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The QuditML project team from left to right: Dr. Michael Giretzlehner (Head of Research Unit Medical Informatics), Dominik Freinberger MSc (Researcher & Developer), DI Philipp Moser PhD (Senior Researcher & Developer), \u00a9 RISC Software GmbH, Reprint free of charge<\/p>\n<\/div>\n<\/div>\n\n\n\n

RISC Software GmbH is pleased to announce the approval of the QuditML research project. The project is entitled “Quantum Machine Learning using Multi-Level Systems” and will receive funding as part of the FWF’s “1000 Ideas” call. It will receive a total budget of EUR 176,096.03 from the Austrian Science Fund (FWF). The project is scheduled to start on October 1, 2025 and will run for 24 months. Philipp Moser, PhD, is the scientific director of the project. <\/p>\n\n\n

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From qubits to quudits: Multidimensional systems for more efficiency<\/strong><\/h3>\n\n\n\n

With QuditML, the project is pursuing a pioneering approach. While previous Quantum Machine Learning (QML) methods rely on binary quantum bits (qubits), QuditML uses so-called quudits that can assume more than two states. Thanks to these multi-level systems, we achieve a higher information density per unit. They also open up new algorithmic possibilities, particularly in terms of efficiency, robustness and resource conservation. <\/p>\n<\/div>\n<\/div>\n\n

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Variety of applications: from medical data to high-energy physics<\/strong><\/h3>\n\n\n\n

The aim of the project is to develop innovative QML algorithms based on Qudits. To this end, we are researching advanced variational and kernel-based methods and applying them to real classification and regression tasks. These tasks come from the fields of medicine, high-energy physics and chemistry and also include tabular data, time series and image data. <\/p>\n<\/div>\n<\/div>\n\n

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Proven quantum expertise as the basis for new research impulses<\/strong><\/h3>\n\n\n\n

RISC Software GmbH can draw on sound previous experience in the field of quantum machine learning. For example, the successfully completed FFG project QML4Med has already shown how quantum methods can contribute to the analysis of medical data. With QuditML, this expertise is now being raised to a new technological level, which<\/strong> will make a significant contribution to the further development of quantum informatics. <\/p>\n\n\n\n

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<\/path><\/svg><\/span> Project in the FWF Research Radar<\/a><\/div>\n\n\n\n
<\/path><\/svg><\/span> Press release as PDF<\/a><\/div>\n<\/div>\n<\/div>\n<\/div>\n\n
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Read more<\/h2>\n\n\n
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