Horizon Europe Avrupa Birliği
Neutral-Atom Platform Technologies
This action aims to establish European leadership in neutral-atom quantum simulation and digital quantum computing by delivering: • Fully programmable industry-ready quan…
Horizon Europe · Avrupa Birliği
JU Eurohpc QML — Quantum Machine Learning
Açık
AB Ufuk Avrupa programı tarafından 2 hibe bekleniyor; proje başına €2 milyon – €3 milyon; toplam bütçe €6 milyon. Başvurular 28 Ocak 2027 tarihinde kapanıyor (AB çağrıları genellikle Brüksel saatiyle 17:00’de kapanır). AB üye ülkelerindeki ve Türkiye gibi ortak ülkelerdeki kuruluşlar genellikle katılabilir; konu koşulları belirleyicidir.
- Son başvuru
- 28 Ocak 2027 (74 gün kaldı)
- Hibe tutarı
- Proje başına €2 milyon – €3 milyon
- Toplam bütçe
- €6 milyon
- Destek oranı
- Uygun maliyetlerin %100’ü Bu program için standart kural; çağrı farklı olabilir.
- Toplam bütçe
- €6 milyon
- Beklenen hibe sayısı
- 2
- Faaliyet türü
- HORIZON JU Research and Innovation Actions · HORIZON Action Grant Budget-Based
- Olası başvuru sahipleri
- Şirketler, KOBİ’ler / girişimler, Kâr amacı gütmeyenler, Üniversiteler ve araştırma, Kamu kurumları Kaynak kategorilerinden veya program kurallarından; uygunluğa çağrı karar verir.
- Fon sağlayıcı
- Horizon Europe (HORIZON)
- Referans
- HORIZON-JU-EUROHPC-2026-QML-07-01
- Çağrı
- HORIZON-JU-EUROHPC-2026-QML-07
- Son kontrol
- 14 Kasım 2026
Resmi açıklamalar fon sağlayıcının dilindedir (çoğunlukla İngilizce); bu sayfadaki özet ve temel bilgiler Türkçedir.
Beklenen sonuçlar
• 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,
Tam metni oku
• 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.
Kapsam
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
Tam metni oku
• 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…
Kimler başvurabilir
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.
Başvuru sahibi kategorileri yalnızca bir başlangıç noktasıdır. Uygunluğa resmi çağrı karar verir.
Zamanlama
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).
Nasıl başvurulur
- Portaldaki konunun tamamını okuyun: beklenen sonuçlar, kapsam, faaliyet türü ve ülke veya konsorsiyum kısıtlamaları dahil uygunluk koşulları.
- Kuruluşunuzu AB Funding & Tenders Portalına kaydederek her ortak için bir PIC numarası alın.
- İşbirliğine dayalı araştırma ve yenilik projeleri genellikle üç farklı AB üye ülkesi veya ortak ülkeden en az üç bağımsız ortak gerektirir (Türkiye Ufuk Avrupa’ya ortak ülkedir). Bu konudaki kuralı doğrulayın.
- Bölüm A’yı (portalda doldurulan idari formlar ve bütçe) ve Bölüm B’yi (bu faaliyet türü için resmi şablondaki teknik açıklama) hazırlayın.
- Son başvuru günü kapanış saatinden önce portal üzerinden gönderin. Geç başvurular kabul edilmez.
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.
Resmi kaynaklar
Bu sayfadaki bilgiler resmi kaynaktan alınmıştır ve en son 14 Kasım 2026 tarihinde kontrol edilmiştir. Sonraki değişiklikler dahil, fon sağlayıcının kendi belgeleri her zaman önceliklidir.
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