Horizon Europe · Europäische Union

JU Eurohpc QML — Quantum Machine Learning

Offen

Das EU-Programm Horizon Europe plant 2 Förderungen mit 2 Mio. € – 3 Mio. € pro Projekt bei einem Gesamtbudget von 6 Mio. €. Die Einreichungsfrist endet am 28. Januar 2027 (EU-Ausschreibungen enden meist um 17:00 Uhr Brüsseler Zeit). Organisationen aus EU-Mitgliedstaaten und assoziierten Ländern wie der Türkei können in der Regel teilnehmen; maßgeblich sind die Bedingungen des Topics.

Frist: 28. Januar 2027 (in 74 Tagen)
Fördersumme: 2 Mio. € – 3 Mio. € pro Projekt
Gesamtbudget: 6 Mio. €
Förderquote: 100 % der förderfähigen Kosten Standardregel des Programms; die Ausschreibung kann abweichen.
Gesamtbudget: 6 Mio. €
Erwartete Förderungen: 2
Maßnahmentyp: HORIZON JU Research and Innovation Actions · HORIZON Action Grant Budget-Based
Wahrscheinliche Antragstellende: Unternehmen, KMU / Start-ups, Gemeinnützige, Hochschulen & Forschung, Öffentliche Stellen Aus Quellkategorien oder Programmregeln; maßgeblich ist die Ausschreibung.
Fördergeber: Horizon Europe (HORIZON)
Referenz: HORIZON-JU-EUROHPC-2026-QML-07-01
Ausschreibung: HORIZON-JU-EUROHPC-2026-QML-07
Zuletzt geprüft: 14. November 2026

Offizielle Ausschreibung öffnen ↗ Frist zum Kalender hinzufügen

Die offiziellen Beschreibungen sind in der Sprache des Fördergebers (meist Englisch); Zusammenfassung und Eckdaten auf dieser Seite sind auf Deutsch.

Erwartete Ergebnisse

• 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,

Vollständigen Text lesen

• 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.

Umfang

Proposals are expected to address multiple key research directions in Quantum Machine Learning (QML), targeting both scientific excellence and industrial relevance. Proposals should clearly outline how to contribute to the development, validation and demonstration of quantum-enhanced AI approaches, with clear pathways towards practical applications. The proposed work should strengthen Europe’s scientific and technological capabilities in quantum computing and accelerate the industrial uptake of quantum-enhanced AI solutions.

Activities may include, but are not limited to

• design and analysis of quantum, quantum-inspired or hybrid QML algorithms,

• performance modelling, complexity analysis and benchmarking of quantum-enhanced AI methods,

• development of error-mitigation and noise-aware strategies tailored to QML workloads,

Proposals should advance scalable QML algorithms capable of addressing large-scale, computationally intensive problems, this includes approaches that

Vollständigen Text lesen

• can manage massive data volumes and complex computational tasks,

• enable faster data processing and improved predictive performance in relevant application domains (e.g. hydrologic research, climate modelling, terrain classification from satellite remote sensing, drug discovery, and image-based medical diagnosis).

Many current QML methods remain closely inspired by classical algorithms and therefore do not yet achieve genuine quantum advantage, requiring further developments in

• quantum-native learning models,

• efficient quantum kernels and quantum feature mappings,

• algorithms demonstrating provable or empirical advantages over classical approaches.

Proposals may also include formal complexity analyses, identification of problem classes that can benefit from quantum acceleration.

Developments should address multiple of the following key research directions :

Quantum Supervised Learning (QSL) :

Quantum Supervised Learning investigates how quantum algorithms can accelerate or improve the training of supervised learning models, offering novel opportunities to explore quantum–classical learning theories and enabling industry to shorten development cycles and enhance performance in data-intensive domains such as finance, healthcare, and Earth observation. Integrating QSL into existing pipelines may help overcome computational bottlenecks and enable more efficient processing of high-dimensional data

• Proposals should explore quantum algorithms and quantum subroutines that can be integrated into classical AI pipelines to mitigate computational bottlenecks, improve efficiency in high-dimensional or large-scale settings, and deliver measurable performance gains. Activities should include the development and validation of such methods, with demonstrations in relevant academic or industrial use cases.

Quantum Convolutional Neural Networks (QCNNs)

Quantum Convolutional Neural Networks combine the conceptual strengths of classical convolutional architectures with the computational…

Wer kann sich bewerben?

Country restriction: participation in this topic is limited to legal entities established in Member States, associated countries to Horizon Europe and EuroHPC JU Participating States.

Antragstellerkategorien sind nur ein Ausgangspunkt. Über die Antragsberechtigung entscheidet die offizielle Ausschreibung.

Zeitplan

Single-stage call. Next deadline 2027-01-28. The portal states the exact cut-off time (EU calls usually close at 17:00 Brussels time).

So bewerben Sie sich

Lesen Sie das vollständige Topic im Portal: erwartete Ergebnisse, Umfang, Maßnahmentyp und die Teilnahmebedingungen einschließlich Länder- oder Konsortialvorgaben.
Registrieren Sie jede Partnerorganisation im EU Funding & Tenders Portal, um einen PIC zu erhalten.
Verbundprojekte in Forschung und Innovation benötigen in der Regel mindestens drei unabhängige Partner aus drei verschiedenen EU-Mitgliedstaaten oder assoziierten Ländern. Prüfen Sie die Regel für dieses Topic.
Bereiten Sie Teil A (Verwaltungsformulare und Budget im Portal) und Teil B (technische Beschreibung auf der offiziellen Vorlage für diesen Maßnahmentyp) vor.
Reichen Sie vor dem Fristende am Stichtag im Portal ein. Verspätete Anträge werden nicht angenommen.

From the call text

Who can apply: Not stated in the call text we hold — check the official page
Company stage: Not stated in the call text we hold — check the official page
Single applicant or consortium: Read the topic conditions on the EU Funding & Tenders Portal for eligible countries, consortium requirements, budget and the application route.
Where applicants must be based: Read the topic conditions on the EU Funding & Tenders Portal for eligible countries, consortium requirements, budget and the application route.
Where to apply: Read the topic conditions on the EU Funding & Tenders Portal for eligible countries, consortium requirements, budget and the application route.

EU-Leitfaden zur Antragstellung lesen

Offizielle Quellen

Offizielle Ausschreibungsseite

Die Angaben auf dieser Seite stammen aus der offiziellen Quelle und wurden zuletzt am 14. November 2026 geprüft. Maßgeblich sind immer die Unterlagen des Fördergebers einschließlich späterer Änderungen.