Quantum Machine Learning

Expected Outcome:

• Integration of quantum computing into data pre-processing pipelines and learning workflows for data-heavy or computationally intensive tasks, demonstrating clear improvements in processing speed, computational complexity, modelling accuracy, and reduced sample requirements at scales achievable with NISQ-era devices,
• Reliable and scalable Quantum Machine Learning (QML) models and algorithms, integrated with existing AI frameworks and pipelines, enabling faster data processing, improved prediction accuracy, and enhanced computational capabilities,
• Validated quantum-enhanced AI methods demonstrating measurable improvements over classical baselines in terms of speed, accuracy, data efficiency, complexity, or scalability, supported by rigorous benchmarking and complexity analysis,
• Robust, noise-aware QML techniques suitable for NISQ hardware, including error-mitigation strategies and algorithmic adaptations that improve reliability, performance, and reproducibility on real quantum processors,
• Demonstrators or proof-of-concept applications showcasing the relevance of QML for real-world challenges (e.g. climate and environmental modelling, Earth observation, healthcare and life sciences, materials discovery, finance, robotics, manufacturing, and cybersecurity),
• Strengthened European leadership and technological sovereignty in quantum computing and trustworthy AI, supported by cross-sector collaboration, knowledge transfer, and contributions to emerging standards, benchmarks and best practices.
• Enhanced collaboration across quantum computing, machine learning and application domains, fostering a coordinated European QML research and innovation community.

Scope:

Trimmed here, the full scope, expected outcomes and award criteria are on the official topic page ↗.