Quantum Machine Learning

With the objective of advancing and unlocking new capabilities for data processing, optimisation, and modelling, the expected outcomes  of this call are meant to:

• Use hybrid approaches by combining quantum processors with classical HPC systems which can address computational bottlenecks while maintaining scalability and robustness , are expected to
• Contribute to development, validation, and demonstration of Quantum Machine Learning (QML) methods, including novel quantum, quantum-inspired, or hybrid algorithms, performance benchmarking, and noise-aware strategies. 

Particular emphasis is placed on scalable solutions capable of handling large and complex datasets, as well as on the development of quantum-native learning models that can demonstrate clear advantages over classical approaches. 

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