Project Description
Project name: QATALYZE
Project description: To contribute to the development and industrialisation of quantum computing applied to computational chemistry, through a platform and hybrid quantum-classical algorithms for the discovery of new catalysts and advanced materials.
The aim of QATALYZE is to build a Quantum Machine Learning as a Service (QMLaaS) platform, as well as the algorithms that power it. The work centres on three use cases: copper-based binary catalysts, MOF structures for capturing CO₂ from the air (DAC) and the screening of pharmacological compounds using QSAR methodologies. In all these cases, quantum generative adversarial networks (QGANs), variational algorithms, tensor networks and classical AI are combined, and it is the system itself that decides which mix of quantum and classical hardware is best suited to each problem. The aim is to reduce the need for costly simulations and laboratory testing in the chemical, energy and biotechnology sectors.
CESGA is participating in this work alongside Gradiant, under the coordination of QCentroid Labs. Its teams are involved in the design of hybrid and quantum-inspired variational algorithms and their adaptation to CESGA’s quantum computing ecosystem. The centre is also integrating its supercomputing and quantum computing infrastructures into the platform, so that third parties can run the models in an interoperable, scalable and secure manner.
Funded by: MCIU – State Research Agency and the European Commission.
Start date: 1 November 2025
End date: 30 October 2028
