Horizon Europe European Union
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 · European Union
JU Eurohpc QML — Quantum Machine Learning
Open
The EU's Horizon Europe programme expects to make 2 awards of €2 million – €3 million each, from a total budget of €6 million. Applications close on 28 Jan 2027 (EU calls usually close at 17:00 Brussels time). Organisations in EU member states and Horizon Europe associated countries such as Türkiye can usually take part; the topic conditions control.
- Deadline
- 28 Jan 2027 (in 74 days)
- Grant amount
- €2 million – €3 million per project
- Total budget
- €6 million
- Funding rate
- 100% of eligible costs Standard rule for this programme; the call can differ.
- Total budget
- €6 million
- Expected awards
- 2
- Type of action
- HORIZON JU Research and Innovation Actions · HORIZON Action Grant Budget-Based
- Likely applicant types
- Companies, SMEs / start-ups, Non-profits, Universities & research, Public bodies From source categories or programme rules; the call decides.
- Funder
- Horizon Europe (HORIZON)
- Reference
- HORIZON-JU-EUROHPC-2026-QML-07-01
- Call
- HORIZON-JU-EUROHPC-2026-QML-07
- Last checked
- 14 Nov 2026
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,
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• 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
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
Read the full text
• 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…
Who can apply
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.
Applicant categories are a starting point. The official call decides eligibility.
Timing
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).
How to apply
- Read the full topic on the Funding & Tenders Portal: expected outcome, scope, type of action and the topic conditions, including any country restrictions.
- Register each participating organisation in the Funding & Tenders Portal to get a PIC.
- Check the topic conditions for the minimum number of partners.
- Prepare Part A (administrative forms and budget, completed in the portal) and Part B (the technical description on the official template for this type of action).
- Submit in the portal before the cut-off time on the deadline day. Late submissions are not accepted.
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.
Where these facts come from
| Fact | Value | How we know | Taken from the source |
|---|---|---|---|
| Deadline | 28 January 2027 “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).” | official data field | 15 Nov 2026 |
| Amount per applicant | Unknown — not stated in the source we hold | unknown | — |
| Total budget | €6 million | official data field | 15 Nov 2026 |
| Funding rate | 100% of eligible costs | programme standard rule | 15 Nov 2026 |
| Who can apply | 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. | read from the official text | 15 Nov 2026 |
| Where to apply | Read the topic conditions on the EU Funding & Tenders Portal for eligible countries, consortium requirements, budget and the application route. | read from the official text | 15 Nov 2026 |
Official sources
Facts on this page come from the official source and were last checked 14 Nov 2026. The funder's own documents, including later amendments, always take precedence.
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