Quantum Machine Learning

Open EU call topic HORIZON-JU-EUROHPC-2026-QML-07-01 · HORIZON JU Research and Innovation Actions. Part of call HORIZON-JU-EUROHPC-2026-QML-07: Quantum Machine Learning. Next deadline 2027-01-28. Topic page: https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/HORIZON-JU-EUROHPC-2026-QML-07-01

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…