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"],"keywords":["HORIZON-JU-EUROHPC-2026-QML-07-01","HORIZON-JU-EUROHPC-2026-QML-07"],"sortStatus":["1"],"language":["en"],"topicConditions":["The conditions are described in the General Annex A of the Horizon Europe Work Programme 2026-2027.
The page limit of the application is 70 pages.
Described in Annex B of the Work Programme General Annexes.
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon Europe projects. See the information in the Horizon Europe Programme Guide.
The conditions are described in the General Annex B of the Horizon Europe Work Programme 2026-2027.
A number of non-EU/non-Associated Countries that are not automatically eligible for funding have made specific provisions for making funding available for their participants in Horizon
Europe projects. See the information in the Horizon Europe Programme Guide.
In order to achieve the expected outcomes, and safeguard the Union’s strategic assets, interests, autonomy, and security, participation in this topic is limited to legal entities established in Member States, associated countries to Horizon Europe and EuroHPC JU Participating States. Proposals including legal entities which are not established in these countries[[Albania, Armenia, Austria, Belgium, Bosnia-Herzegovina, Bulgaria, Croatia, Cyprus, Czechia, Denmark, Estonia, Faroe Islands, Finland, France, Germany, Georgia, Greece, Hungary, Iceland, Ireland, Israel, Italy, Kosovo, Latvia, Lithuania, Luxembourg, Malta, Moldova, Montenegro, Netherlands, North Macedonia, Norway, Poland, Portugal, Romania, Serbia, Slovakia, Slovenia, Spain, Sweden, Switzerland, Türkiye, United Kingdom, and all countries that automatically eligible for funding under Horizon Europe as provided in list-3rd-country-participation_horizon-euratom_en.pdf (V3.5 – 20.08.2025).]] will be ineligible.
This decision has been taken on the grounds that, in the area of research covered by this topic, EU open strategic autonomy is particularly at stake. It is important to avoid a situation of technological dependency on a non-EU source, in a global context that requires the EU to take action to build on its strengths, and to carefully assess and address any strategic weaknesses, vulnerabilities and high-risk dependencies which put at risk the attainment of its ambitions.
Described in Annex B of the Work Programme General Annexes.
Described in Annex C of the Work Programme General Annexes.
The procedure is described in the General Annex F of the Horizon Europe Work Programme 2026-2027.
Eligible proposals submitted under this topic and exceeding all the evaluation thresholds will be awarded a STEP Seal [[https://strategic-technologies.europa.eu/about/step-seal_en]].
Described in Annex D of the Work Programme General Annexes.
The general criteria are described in the General Annex D of the Horizon Europe Work Programme 2026-2027.
described in Annex F of the Work Programme General Annexes and the Online Manual.
described in Annex F of the Work Programme General Annexes.
The rules are described in General Annex G of the of the Horizon Europe Work Programme 2026-2027. The following exceptions apply:
As an exception from General Annex G of the Horizon Europe Work Programme, the EU-funding rate for eligible costs in grants awarded by the JU for this topic will be up to 50% of the eligible costs.
Expected project duration: 48 months
The documents are described in the General Annex E of the Horizon Europe Work Programme 2026-2027.
Application form templates
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Evaluation form templates — will be used with the necessary adaptations
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Standard evaluation form (HE RIA, IA and CSA Stage 1)
Standard evaluation form (HE RIA, IA and CSA Stage 1 BLIND)
Standard evaluation form (HE PCP PPI)
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Standard evaluation form (HE EIC PATHFINDER CHALLENGES)
Standard evaluation form (HE EIC PATHFINDER OPEN)
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Guidance
Model Grant Agreements (MGA)
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Call-specific instructions
Information on financial support to third parties (HE)
EuroHPC Work Programme 2026 (Decision 22/2026 Amendment No 3)
HE Main Work Programme 2026-2027 – 1. General Introduction
HE Main Work Programme 2026-2027 – 2. Marie Skłodowska-Curie Actions (MSCA)
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HE Main Work Programme 2026-2027 – 15. General Annexes
HE Framework Programme and Rules for Participation Regulation 2021/695
HE Specific Programme Decision 2021/764
EU Financial Regulation 2024/2509
Decision authorising the use of lump sum contributions under the Horizon Europe Programme
Rules for Legal Entity Validation, LEAR Appointment and Financial Capacity Assessment
EU Grants AGA — Annotated Model Grant Agreement
Funding & Tenders Portal Online Manual
Expected Outcome:
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
Proposals should advance scalable QML algorithms capable of addressing large-scale, computationally intensive problems, this includes approaches that
Many current QML methods remain closely inspired by classical algorithms and therefore do not yet achieve genuine quantum advantage, requiring further developments in
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
Quantum Convolutional Neural Networks (QCNNs)
Quantum Convolutional Neural Networks combine the conceptual strengths of classical convolutional architectures with the computational advantages of quantum processing, offering a promising testbed for exploring expressivity and efficiency in hybrid models and providing industry with a practical pathway to near-term quantum advantage by using QPUs during training while retaining classical inference for scalability and compatibility with existing AI systems
Learning with Quantum Models
Quantum models integrate quantum computation directly into both training and inference through parametrised quantum circuits and quantum kernels, enabling advances in understanding quantum data representations, feature expressivity and generalisation theory, while offering industry benefits such as reduced model complexity, faster training cycles and lower data requirements - particularly in data-scarce or high-value domains (e.g. drug discovery, autonomous systems, materials engineering)
Quantum Reinforcement Learning (QRL)
Quantum Reinforcement Learning investigates how quantum computing can improve the efficiency and scalability of reinforcement learning, offering researchers opportunities to rethink RL fundamentals in high-dimensional or continuous state spaces and providing industry with the potential for faster policy training and more effective exploration strategies (e.g. in robotic, logistics optimisation and adaptive control)
Quantum Unsupervised Learning
Quantum unsupervised learning explores quantum algorithms capable of detecting latent structures, clusters and patterns in unlabelled data, offering new theoretical insights into quantum-enhanced data analysis and providing industry with potential benefits such as polynomial speedups in tasks like anomaly detection, customer segmentation and predictive maintenance, particularly for complex or high-dimensional datasets where classical methods become computationally prohibitive
Quantum AI for Algorithmic Discovery
Quantum AI for algorithmic discovery combines quantum computing with classical machine learning to design new algorithms and computational strategies, enabling researchers to explore vast algorithmic search spaces and develop fundamentally new quantum or hybrid approaches, while offering industry a pathway to automated discovery of optimised algorithms for simulation, optimisation and data-driven modelling (e.g. advanced manufacturing, chemical engineering, cybersecurity and energy systems)
Collaboration between academia, research infrastructures, industry, SMEs and actors across the European quantum ecosystem is strongly encouraged to ensure wide applicability, rapid technological uptake and sustainable long-term impact. Proposals should demonstrate how the collaborative approach and resulting innovations will contribute to strengthening Europe’s leadership in quantum technologies and in trustworthy, high-performance artificial intelligence.
The proposal must also outline a clear IP plan and licensing strategy under the European Union Public Licence (EUPL-1.2) to safeguard openness and promoting European industrial uptake enabling first exploitation within EU- and Participating States.
Requirements:
Technology Readiness Level - Technology readiness level expected from completed projects
The rules are described in General Annex B of the Horizon Europe Work Programme 2026-2027.
Activities are expected to start at TRL 3/4 and to achieve TRL 5/6 by the end of the project
Online Manual is your guide on the procedures from proposal submission to managing your grant.
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The European Charter for Researchers and the Code of Conduct for their recruitment – consult the general principles and requirements specifying the roles, responsibilities and entitlements of researchers, employers and funders of researchers.
Partner Search help you find a partner organisation for your proposal.
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