SpaceCom/docs/Tenders/past tenders/tmp_pdf_extracts/QUANTAIR Proposal-SEP-211215087.txt

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Administrative forms
Page 1 of 33 Last saved 17/09/2025 06:50Horizon Europe ver 1.00 20241022
Call: HORIZON-SESAR-2025-DES-ER-03 
 (Digital European Sky Exploratory Research 03)
Topic: HORIZON-SESAR-2025-DES-ER-03-WA1-3
Type of Action: HORIZON-JU-RIA  
  (HORIZON JU Research and Innovation Actions)
Proposal number: 101289612
Proposal acronym: QUANTAIR
Type of Model Grant Agreement: HORIZON Lump Sum Grant
Table of contents
Section Title Action
1 General information
2 Participants
3 Budget
4 Ethics and security
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
1 - General information
Fields marked * are mandatory to fill.
Topic HORIZON-SESAR-2025-DES-ER-03-WA1-3
Type of Model Grant Agreement HORIZON-AG-LS
Type of Action HORIZON-JU-RIA
Call HORIZON-SESAR-2025-DES-ER-03
Acronym QUANTAIR
Proposal title Quantum Technologies for Airspace Innovation and Resilience
Note that for technical reasons, the following characters are not accepted in the Proposal Title and will be removed: < > " &
Duration in 
months 24
Free keywords
Quantum Federated Learning, Quantum Machine Learning, Data Security, Privacy preservation, Demand Capacity 
Balancing, ETOT optimisation, Improve Predictability of ATM operations
Abstract *
European air traffic management is becoming increasingly complex. The system now has to handle a growing variety of airspace 
users — from hypersonic vehicles and high-altitude long-endurance platforms such as stratospheric balloons and HAPS, to 
conventional subsonic flights. These vehicles often operate in overlapping altitude bands but have vastly different speeds, climb/
descent profiles, and manoeuvring capabilities. 
 
At the same time, environmental policy drivers are stronger than ever. The EU Green Deal, ICAO’s long-term aspirational goals, and 
national climate strategies are pushing for measurable reductions in both CO₂ and non-CO₂ impacts, such as persistent contrails. 
Resilience has also become a priority, with the network increasingly affected by severe weather, technical failures, and geopolitical 
events that can close or restrict airspace at short notice. 
 
One of the biggest technical challenges in all of these contexts is that many stakeholders — States, ANSPs, airlines, and defence 
operators — cannot freely share operationally, privacy, or commercially sensitive data. Without that data, current modelling and 
optimisation tools have to work with partial information, limiting their effectiveness. 
 
Previous European research has already demonstrated that Federated Learning (FL) can bridge this gap, enabling accurate 
predictions without requiring data to leave its origin. QUANTAIR proposes to take this further by pairing FL with quantum 
optimisation — allowing us to integrate richer, privacy-protected data from multiple stakeholders, and then solve the resulting large-
scale, multi-variable problems at speeds suitable for operational decision-making.
Remaining characters 315
Has this proposal (or a very similar one) been submitted in the past 2 years in response to a call for 
 proposals under any EU programme, including the current call?
Yes No
Please give the proposal reference or contract number.
Previously submitted proposals should be with either 6 or 9 digits.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
Declarations
Field(s) marked * are mandatory to fill.
1)  We declare to have the explicit consent of all applicants on their participation and on the content of this proposal. *
2) We confirm that the information contained in this proposal is correct and complete and that none of the project 
activities have started before the proposal was submitted (unless explicitly authorised in the call conditions). *
3) We declare: 
 - to be fully compliant with the eligibility criteria set out in the call 
 - not to be subject to any exclusion grounds under the EU Financial Regulation 2018/1046 
 - to have the financial and operational capacity to carry out the proposed project. *
4) We acknowledge that all communication will be made through the Funding & Tenders Portal 
electronic exchange system and that access and use of this system is subject to the Funding & Tenders Portal Terms 
and Conditions.    *
5) We have read, understood and accepted the Funding & Tenders Portal Terms & Conditions and  
Privacy Statement that set out the conditions of use of the Portal and the scope, purposes, retention periods, etc. for 
the processing of personal data of all data subjects whose data we communicate for the purpose of the application, 
evaluation, award and subsequent management of our grant, prizes and contracts (including financial transactions and 
audits). *
6) We declare that the proposal complies with ethical principles (including the highest standards of research integrity 
as set out in the ALLEA European Code of Conduct for Research Integrity, as well as applicable international and 
national law, including the Charter of Fundamental Rights of the European Union and the European Convention on 
Human Rights and its Supplementary Protocols. Appropriate procedures, policies and structures are in place to foster 
responsible research practices, to prevent questionable research practices and research misconduct, and to handle 
allegations of breaches of the principles and standards in the Code of Conduct. *
7) We declare that the proposal has an exclusive focus on civil applications (activities intended to be used in military 
application or aiming to serve military purposes cannot be funded). If the project involves dual-use items in the sense 
of  Regulation 2021/821, or other items for which authorisation is required, we confirm that we will comply with the 
applicable regulatory framework (e.g. obtain export/import licences before these items are used). *
8) We confirm that the activities proposed do not  
- aim at human cloning for reproductive purposes; 
- intend to modify the genetic heritage of human beings which could make such changes heritable  
  (with the exception of research relating to cancer treatment of the gonads, which may be financed), or 
- intend to create human embryos solely for the purpose of research or for the purpose of stem  
  cell procurement, including by means of somatic cell nuclear transfer. 
- lead to the destruction of human embryos (for example, for obtaining stem cells) 
These activities are excluded from funding. *
9) We confirm that for activities carried out outside the Union, the same activities would have been allowed in at least 
one EU Member State. *
10) For Lump Sum Grants with a detailed budget table: We understand and accept that the EU lump sum grants must 
be reliable proxies for the actual costs of a project and confirm that the detailed budget for the proposal has been 
established in accordance with our usual cost accounting practices and in compliance with the basic eligibility 
conditions for EU actual cost grants (see AGA - Annotated Grant Agreement, art 6) and exclude costs that are ineligible 
under the Programme. Purchases and subcontracting costs must be done taking into account best value for money 
and must be free of conflict of interest. *
The coordinator is only responsible for the information relating to their own organisation. Each applicant remains responsible for the information declared for 
their organisation. If the proposal is retained for EU funding, they will all be required to sign a declaration of honour. 
False statements or incorrect information may lead to administrative sanctions under the EU Financial Regulation.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
2 - Participants
List of participating organisations
# Participating Organisation Legal Name Country Role Action
1 DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV Germany Coordinator
2 Qoro Quantum Ltd UK Partner
3 SkyNav Europe BE Partner
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Organisation data
PIC
999981731
Legal name
DEUTSCHES ZENTRUM FUR LUFT - UND RAUMFAHRT EV
Short name: DLR 
 
Address
Town KOLN
Postcode 51147
Street LINDER HOHE
Country Germany
Webpage www.dlr.de
Specific Legal Statuses
Legal person .......................................................... yes
Public body ............................................................ no
Non-profit ............................................................... yes
International organisation ...................................... no
Secondary or Higher education establishment ...... no
Research organisation ........................................... yes
SME Data
Based on the below details from the Participant Registry the organisation is not an SME (small- and medium-sized enterprise) for the call.
SME self-declared status ...................................... 03/01/2022 - no
SME self-assessment ........................................... unknown
SME validation ...................................................... 28/10/2008 - no
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Departments carrying out the proposed work
Department 1
Department name Space Operations and Astronaut Training
Street Münchener Straße 20
Town Oberpfaffenhofen-Weßling
Same as proposing organisation's address
not applicable
Country Germany
Postcode 82234
Links with other participants
Type of link Participant
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Main contact person
This will be the person the EU services will contact concerning this proposal (e.g. for additional information, invitation to hearings, sending of 
evaluation results, convocation to start grant preparation). The data in blue is read-only. Details (name, first name and e-mail) of Main Contact 
persons should be edited in the step "Participants" of the submission wizard.
First name* Andreas Last  name* Spörl
E-Mail* andreas.spoerl@dlr.de
Town Oberpfaffenhofen-Weßling Post code 82234
Street Münchener Straße 20
Website Please enter website
Position in org. group lead
Department Space Operations and Astronaut Training
Phone +xxx xxxxxxxxx Phone 2 +xxx xxxxxxxxx
Gender Woman Man Non BinaryTitle Dr
Same as proposing organisation's address
Country  Germany
Same as organisation 
name
Other contact persons
First Name Last Name E-mail Phone
Catharina Broocks catharina.broocks@dlr.de +xxx xxxxxxxxx
Sylvia Hutzschenreuter sylvia.hutzschenreuter@dlr.de +xxx xxxxxxxxx
Mirjam Kopp mirjam.kopp@dlr.de +xxx xxxxxxxxx
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Researchers involved in the proposal
Title First Name Last Name Gender Nationality E-mail Career Stage
Role of 
researcher (in 
the project)
Reference 
Identifier Type of identifier
Dr Andreas Spörl Man Germany andreas.spoerl@
dlr.de Category B  Senior resea Leading 0009-0003-0727-
440X
Orcid ID
Ms Catharina Broocks Woman Germany catharina.broock
s@dlr.de
Category D  First stage r Team member 0000-0002-4965-
1934
Orcid ID
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Role of participating organisation in the project
Project management
Communication, dissemination and engagement
Provision of research and technology infrastructure
Co-definition of research and market needs
Civil society representative
Policy maker or regulator, incl. standardisation body
Research performer
Technology developer
Testing/validation of approaches and ideas
Prototyping and demonstration
IPR management incl. technology transfer
Public procurer of results
Private buyer of results
Finance provider (public or private)
Education and training
Contributions from the social sciences or/and the humanities
Other 
If yes, please specify: (Maximum number of characters allowed: 50)
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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List of up to 5 publications, widely-used datasets, software, goods, services, or any other achievements relevant to the call content.
Type of achievement Short description (Max 500 characters)
Publication
"Evolving Spacecraft Quantum On-Call Scheduling" (Prüfer S., Sajko W. et al (DLR), 
SpaceOps-2023, ID 388) explores applying Grover's algorithm on quantum computers to 
optimize a simplified operator shift scheduling problem at the German Space Operations 
Center (GSOC). The study of this combinatorial optimization problem improves constraint 
handling and achieves reduced gate costs of up to 90%. While focused on spacecraft, these 
methods are also applicable to other scheduling tasks.
Publication
"QUARGS – Quantum Reinforced Ground Station Scheduling" (Leidreiter D. A., Petrak A., et al 
(DLR); SpaceOps-2025; ID 267) investigates the application of quantum computing to optimize 
ground station scheduling for satellite operations. It introduces the QUARGS library that 
utilizes quantum algorithms like QAOA, QA, VQE, and E-VQE. The study compares these 
methods to classical solvers and addresses challenges in scalability and constraint 
implementation.
Publication
“QEOPS – Quantum Earth Observation Planning System” (Prüfer S., Anderle M. A. (DLR); IWPSS 
2025) discusses a spacecraft mission planning use-case from the Quantum Mission Planning 
Challenges (QMPC) project by the German Quantum Computing Initiative at DLR. It formalizes 
the problem as an Integer Linear Programming (ILP) task, shares insights from applying 
quantum computing, and compares early results between classical solvers and quantum 
optimization algorithms.
Software
QUEASARS - Quantum Evolving Ansatz Variational Solver (Leidreiter D. A., Prüfer S. (DLR), 
https://github.com/DLR-RB/QUEASARS) is an open-source, qiskit-based, python package 
implementing quantum variational eigensolvers which use evolutionary algorithms to find a 
good ansatz during the optimization process, like E-VQE, MoG-VQE or QNEAT. Currently only 
EVQE is implemented.
List of up to 5 most relevant previous projects or activities, connected to the subject of this proposal.
Name of Project or Activity Short description (Max 500 characters)
QMPC
Quantum Mission Planning Challenges (QMPC) is a DLR QCI project (Nov<6F>2022–Oct<63>2026) 
developing quantum algorithms to solve real-world mission planning optimization problems 
such as on call operator scheduling, ground station contact planning, and earth observation 
acquisition for the German Space Operations Center (GSOC). It integrates hybrid and quantum 
methods into GSOC workflows and extends to a Vehicle to Grid use case with the project 
partner E.ON.
MuQuaNet
MuQuaNet (Munich Quantum Network) is a QKD-based quantum network in Munich, 
enabling secure communication for research and government use. Within it, DLR’s QSOC 
integrates QKD into space operations by linking GSOC to the network and testing quantum-
encrypted data transmission. These efforts support future QKD-based space missions and 
extend QSOC's role from quantum computing to secure quantum communication.
QAthMOS
QAthMOS (Quantum Telemetry and Health Monitoring System) is a QSOC led project aiming 
to enhance satellite operations via quantum powered anomaly detection and predictive 
analytics on telemetry data. It applies quantum machine learning techniques to monitor 
satellite health, detect irregularities, and forecast issues—advancing the integration of 
quantum technologies into operational spaceflight systems.
Description of any significant infrastructure and/or any major items of technical equipment, relevant to the proposed work.
Name of infrastructure of 
equipment
Short description (Max 300 characters)
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Quantum computers and 
Simulators
Our connection to the DLR-QCI (Quantum Computing Initiative) allows access to a variety of 
quantum computers and simulators based on different quantum hardware platforms. Small 
scaled demo examples can therefore be computed and executed on actual quantum 
hardware.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Gender Equality Plan
Does the organization have a Gender Equality Plan (GEP) covering the elements listed below? Yes No
Minimum process-related requirements (building blocks) for a GEP 
        -    Publication: formal document published on the institution's website and signed by the top management 
        -    Dedicated resources: commitment of human resources and gender expertise to implement it.  
        -    Data collection and monitoring: sex/gender disaggregated data on personnel (and students for establishments 
 concerned) and annual reporting based on indicators. 
        -    Training: Awareness raising/trainings on gender equality and unconscious gender biases for staff and  
 decision-makers. 
        -    Content-wise, recommended areas to be covered and addressed via concrete measures and targets are:  
o work-life balance and organisational culture; 
o gender balance in leadership and decision-making; 
o gender equality in recruitment and career progression; 
o integration of the gender dimension into research and teaching content; 
o measures against gender-based violence including sexual harassment.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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PIC
870017348
Legal name
Qoro Quantum Ltd
Short name: Qoro Quantum Ltd 
 
Address
Town Harwell
Postcode OX11 0QX
Street R27 Atlas Building, Fermi Avenue, 
Country United Kingdom
Webpage https://qoro.uk
Specific Legal Statuses
Legal person .......................................................... yes
Public body ............................................................ no
Non-profit ............................................................... no
International organisation ...................................... no
Secondary or Higher education establishment ...... no
Research organisation ........................................... no
SME Data
Based on the below details from the Participant Registry the organisation is an SME (small- and medium-sized enterprise) for the call.
SME self-declared status ...................................... 18/08/2025 - yes
SME self-assessment ........................................... unknown
SME validation ...................................................... unknown
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Departments carrying out the proposed work
No department involved
Department name Name of the department/institute carrying out the work.
Street Please enter street name and number.
Town Please enter the name of the town.
Same as proposing organisation's address
not applicable
Country Please select a country
Postcode Area code.
Links with other participants
Type of link Participant
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Main contact person
This will be the person the EU services will contact concerning this proposal (e.g. for additional information, invitation to hearings, sending of 
evaluation results, convocation to start grant preparation). The data in blue is read-only. Details (name, first name and e-mail) of Main Contact 
persons should be edited in the step "Participants" of the submission wizard.
First name* Stephen Last  name* DiAdamo
E-Mail* stephen@qoroquantum.de
Town Harwell Post code OX11 0QX
Street R27 Atlas Building, Fermi Avenue, 
Website www.qoroquantum.net
Position in org. CTO & Co-Founder
Department Qoro Quantum Ltd
Phone +4915254519041 Phone 2 +xxx xxxxxxxxx
Gender Woman Man Non BinaryTitle Dr
Same as proposing organisation's address
Country  United Kingdom
Same as organisation 
name
Other contact persons
First Name Last Name E-mail Phone
Dan Holme dan@qoro.uk +xxx xxxxxxxxx
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Researchers involved in the proposal
Title First Name Last Name Gender Nationality E-mail Career Stage
Role of 
researcher (in 
the project)
Reference 
Identifier Type of identifier
Dr Stephen DiAdamo Man Italy stephen@qoroqu
antum.uk Category B  Senior resea Leading 0000-0001-5758-
9563
Orcid ID
Ms Amana Liaqat Woman United Kingdom amana@qoroqua
ntum.uk
Category C  Recognised Team member
Mr Dan Holme Man United Kingdom dan@qoro.uk Category C  Recognised Leading
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Role of participating organisation in the project
Project management
Communication, dissemination and engagement
Provision of research and technology infrastructure
Co-definition of research and market needs
Civil society representative
Policy maker or regulator, incl. standardisation body
Research performer
Technology developer
Testing/validation of approaches and ideas
Prototyping and demonstration
IPR management incl. technology transfer
Public procurer of results
Private buyer of results
Finance provider (public or private)
Education and training
Contributions from the social sciences or/and the humanities
Other 
If yes, please specify: (Maximum number of characters allowed: 50)
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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List of up to 5 publications, widely-used datasets, software, goods, services, or any other achievements relevant to the call content.
Type of achievement Short description (Max 500 characters)
Publication
"QAOA in Quantum Datacenters: Parallelization, Simulation, and Orchestration." 2025 IEEE 
International Conference on Quantum Software, 2025. Introduces Qoro’s architecture for 
automated quantum software execution across distributed computing networks. 
Demonstrates orchestration strategies that parallelize workloads and allocate resources 
dynamically, critical foundations for federated quantum computing.
Publication
"Practical quantum k-means clustering: Performance analysis and applications in energy grid 
classification." IEEE Transactions on Quantum Engineering 3, 2022. Demonstrates hardware-
aware quantum machine learning methods, showing performance gains when hardware 
constraints are integrated into software design. Provides evidence for scaling machine 
learning and optimization workloads over heterogeneous quantum-classical infrastructures.
Publication
 "Quantum algorithms and simulation for parallel and distributed quantum computing." 2021 
IEEE/ACM Second International Workshop on Quantum Computing Software. IEEE, 2021. 
Presents a software framework for parallel execution of quantum algorithms across clusters. 
Lays groundwork for distributed execution models, including federated approaches used in 
this project.
Software
Qoro's Divi. Divi is a Python-based software development kit that builds and parallelizes 
quantum programs. It partitions tasks and transmits them to distributed compute resources 
through Composer, enabling large-scale workloads such as optimization and machine 
learning. It supports hybrid execution, key to federated computing.
Software
Qoro's Composer. Composer is a scheduling and orchestration engine that dynamically selects 
resources for distributed workloads. It evaluates real-time network and device conditions to 
route jobs efficiently, forming the backbone of federated learning and computation in hybrid 
HPC-quantum environments.
List of up to 5 most relevant previous projects or activities, connected to the subject of this proposal.
Name of Project or Activity Short description (Max 500 characters)
ESA Business Incubation
Selected for ESA’s incubation program, Qoro applied Divi and Composer to satellite 
communications optimization. Demonstrated distributed computing and task partitioning 
over satellite constellations, proving the scalability and adaptability of our approach for 
mission-critical environments. 
Pilot projects with HPC centers
In partnership with CESGA, we deployed Composer to orchestrate quantum-classical 
workloads over HPC nodes, integrating security and scheduling for multi-node execution. This 
work validated interoperability across heterogeneous systems and demonstrated scaling 
strategies relevant to air traffic management.
Pilot projects with Enterprise
Collaborated with enterprise partners (e.g., E.ON, Multiverse Computing) to integrate Divi into 
energy and finance workflows. Showcased parallelization and partitioning of real-world 
optimization problems over hybrid clusters, demonstrating readiness to bring federated 
models to complex industries.
Description of any significant infrastructure and/or any major items of technical equipment, relevant to the proposed work.
Name of infrastructure of 
equipment
Short description (Max 300 characters)
Qoro's cloud computing service
A scalable cloud platform hosting Qoro’s distributed orchestration stack, enabling 
deployment, benchmarking, and execution of federated quantum-classical workloads across 
multiple sites. 
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Qoro's GPU-based quantum 
simualator
High-performance GPU-accelerated simulator for quantum circuits, optimized for batch 
execution and hybrid workflows, enabling realistic modeling of large-scale federated 
computing scenarios. 
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Gender Equality Plan
Does the organization have a Gender Equality Plan (GEP) covering the elements listed below? Yes No
Minimum process-related requirements (building blocks) for a GEP 
        -    Publication: formal document published on the institution's website and signed by the top management 
        -    Dedicated resources: commitment of human resources and gender expertise to implement it.  
        -    Data collection and monitoring: sex/gender disaggregated data on personnel (and students for establishments 
 concerned) and annual reporting based on indicators. 
        -    Training: Awareness raising/trainings on gender equality and unconscious gender biases for staff and  
 decision-makers. 
        -    Content-wise, recommended areas to be covered and addressed via concrete measures and targets are:  
o work-life balance and organisational culture; 
o gender balance in leadership and decision-making; 
o gender equality in recruitment and career progression; 
o integration of the gender dimension into research and teaching content; 
o measures against gender-based violence including sexual harassment.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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PIC
870906450
Legal name
SkyNav Europe
Short name: SkyNav Europe 
 
Address
Town Brussels
Postcode 1000
Street Rue Coppens 16
Country Belgium
Webpage www.skynavintl.com
Specific Legal Statuses
Legal person .......................................................... yes
Public body ............................................................ no
Non-profit ............................................................... no
International organisation ...................................... no
Secondary or Higher education establishment ...... no
Research organisation ........................................... no
SME Data
Based on the below details from the Participant Registry the organisation is an SME (small- and medium-sized enterprise) for the call.
SME self-declared status ...................................... 27/09/2024 - yes
SME self-assessment ........................................... 27/09/2024 - yes
SME validation ...................................................... unknown
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Departments carrying out the proposed work
No department involved
Department name Name of the department/institute carrying out the work.
Street Please enter street name and number.
Town Please enter the name of the town.
Same as proposing organisation's address
not applicable
Country Please select a country
Postcode Area code.
Links with other participants
Type of link Participant
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Main contact person
This will be the person the EU services will contact concerning this proposal (e.g. for additional information, invitation to hearings, sending of 
evaluation results, convocation to start grant preparation). The data in blue is read-only. Details (name, first name and e-mail) of Main Contact 
persons should be edited in the step "Participants" of the submission wizard.
First name* Ben Last  name* Kings
E-Mail* ben.kings@skynavintl.com
Town Brussels Post code 1000
Street Rue Coppens 16
Website https://skynavintl.com/
Position in org. Managing Director/Owner
Department SkyNav Europe
Phone +31615625092 Phone 2 +xxx xxxxxxxxx
Gender Woman Man Non BinaryTitle Mr
Same as proposing organisation's address
Country  Belgium
Same as organisation 
name
Other contact persons
First Name Last Name E-mail Phone
Duncan Auld duncan.auld@skynavintl.com +xxx xxxxxxxxx
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

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Researchers involved in the proposal
Title First Name Last Name Gender Nationality E-mail Career Stage
Role of 
researcher (in 
the project)
Reference 
Identifier Type of identifier
Mr Ben Kings Man ben.kings@skyna
vintl.com Category A  Top grade reLeading
Mr Duncan Auld Man duncan.auld@sky
navintl.com
Category A  Top grade reLeading
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

=== PAGE 25 ===
Administrative forms
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Role of participating organisation in the project
Project management
Communication, dissemination and engagement
Provision of research and technology infrastructure
Co-definition of research and market needs
Civil society representative
Policy maker or regulator, incl. standardisation body
Research performer
Technology developer
Testing/validation of approaches and ideas
Prototyping and demonstration
IPR management incl. technology transfer
Public procurer of results
Private buyer of results
Finance provider (public or private)
Education and training
Contributions from the social sciences or/and the humanities
Other 
If yes, please specify: (Maximum number of characters allowed: 50)
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=== PAGE 26 ===
Administrative forms
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List of up to 5 publications, widely-used datasets, software, goods, services, or any other achievements relevant to the call content.
Type of achievement Short description (Max 500 characters)
Service
Operational ATM experience 
Decades of global, operational Air Traffic Control experience across all ATC disciplines (Tower, 
Approach, Area, Oceanic) and at all function levels. Operational supervision, flow 
management, training, training management, safety and technical committee 
representation, operational procedure development, international cross-border negotiations, 
airspace design, safety risk assessments and environmental impact studies
Service
Project Management & Leadership Expertise 
Extensive track record in project and organisational leadership, including executive roles 
within IFATCA (International Federation of Air Traffic Controllers’ Associations). Demonstrated 
ability to manage complex international initiatives, coordinate diverse stakeholders, and 
oversee multi-million-euro budgets. Proven experience in steering strategic aviation projects, 
ensuring delivery of innovative outcomes aligned with European policy & industry need
Service
ECHO2 subcontractor 
Participation in the ECHO2 consortium as a contractor focusing on higher airspace and space 
transport integration. Contributions include operational concept refinement, validation 
planning, stakeholder mapping, and alignment with ANSP procedures and Network functions. 
The work informs scalable approaches for trajectory management and airspace reservations. 
Includes project management and deliverable lead.
Service
ICAO drafting and representation 
Contributed to drafting and review activities at ICAO in relation to Annex 11, Annex 10 and 
PANS-ATM material. Work includes requirements structuring, procedure text, and consistency 
checks across datasets and guidance, supporting globally harmonised ATM provisions 
relevant to STO and HAO integration. Leading working groups on ATM planning & 
implementation, development of Global ATM Operational Concept, development of Aviation 
System Block Upgrades
Service
State regulatory drafting and representation 
Several years of regulatory drafting support for a Gulf State authority, updating national civil 
aviation regulations, AMC/GM-style guidance and implementation procedures across ANS, 
operations and oversight. Emphasis on practicality, traceability and alignment with ICAO and 
regional provisions. Leadership of ICAO regional groups and task forces related to integration 
of space transport activities.
List of up to 5 most relevant previous projects or activities, connected to the subject of this proposal.
Name of Project or Activity Short description (Max 500 characters)
SESAR ECHO / ECHO2 – HAO 
Integration
Participation in ECHO and ECHO2 on higher airspace and space transport integration. Roles 
covered operational scenarios, requirements traceability, validation planning, stakeholder 
engagement and alignment with EUROCONTROL and ICAO practices for cross-border 
coordination and dynamic, minimal-impact airspace management.
iNEO – Project Management Plan 
& Governance Appr.
Development of a rigorous PMP and governance model for multi-partner R&D, covering 
schedule baselining, risk and compliance, quality assurance, and reporting. The approach 
underpins efficient WP coordination and is directly reusable for other HORIZON projects.
UAE National Regulations 
Development Programme
Regulatory drafting support for a Gulf State authority, updating national civil aviation 
regulations, AMC/GM-style guidance and implementation procedures across ANS, operations 
and oversight. Emphasis on practicality, traceability and alignment with ICAO and regional 
provisions.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

=== PAGE 27 ===
Administrative forms
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Description of any significant infrastructure and/or any major items of technical equipment, relevant to the proposed work.
Name of infrastructure of 
equipment Short description (Max 300 characters)
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

=== PAGE 28 ===
Administrative forms
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Gender Equality Plan
Does the organization have a Gender Equality Plan (GEP) covering the elements listed below? Yes No
Minimum process-related requirements (building blocks) for a GEP 
        -    Publication: formal document published on the institution's website and signed by the top management 
        -    Dedicated resources: commitment of human resources and gender expertise to implement it.  
        -    Data collection and monitoring: sex/gender disaggregated data on personnel (and students for establishments 
 concerned) and annual reporting based on indicators. 
        -    Training: Awareness raising/trainings on gender equality and unconscious gender biases for staff and  
 decision-makers. 
        -    Content-wise, recommended areas to be covered and addressed via concrete measures and targets are:  
o work-life balance and organisational culture; 
o gender balance in leadership and decision-making; 
o gender equality in recruitment and career progression; 
o integration of the gender dimension into research and teaching content; 
o measures against gender-based violence including sexual harassment.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

=== PAGE 29 ===
Administrative forms
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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
3 - Budget
No Name of Beneficiary Country Role Requested grant 
amount
 Income generated 
by the action
Financial 
contributions
 Own resources  Total estimated 
income 
1 Deutsches Zentrum Fur Luft - Und Raumfahrt Ev DE Coordinator   167 350.00    0    0    0    167 350.00
2 Qoro Quantum Ltd UK Partner   435 086.16    0    0   20 000    455 086.16
3 Skynav Europe BE Partner   342 890.63    0    0    0    342 890.63
Total   945 326.79   20 000
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

=== PAGE 30 ===
Administrative forms
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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
4 - Ethics & security
Ethics Issues Table
1. Human Embryonic Stem Cells and Human Embryos Page
Does this activity involve Human Embryonic Stem Cells (hESCs)? Yes No
Does this activity involve the use of human embryos? Yes No
2. Humans Page
Does this activity involve human participants? Yes No
Does this activity involve interventions (physical also including imaging technology, 
behavioural treatments, etc.) on the study participants? Yes No
Does this activity involve conducting a clinical study as defined by the Clinical Trial Regulation 
(EU 536/2014)? (using pharmaceuticals, biologicals, radiopharmaceuticals, or advanced 
therapy medicinal products)
Yes No
3. Human Cells / Tissues (not covered by section 1) Page
Does this activity involve the use of human cells or tissues? Yes No
4. Personal Data Page
Does this activity involve processing of personal data? Yes No
Does this activity involve further processing of previously collected personal data (including 
use of preexisting data sets or sources, merging existing data sets)?
Yes No
Is it planned to export personal data from the EU to non-EU countries? Yes No
Is it planned to import personal data from non-EU countries into the EU or from a non-EU 
country to another non-EU country?
Yes No
Does this activity involve the processing of personal data related to criminal convictions or 
offences?
Yes No
5. Animals Page
Does this activity involve animals? Yes No
6. Non-EU Countries Page
Will some of the activities be carried out in non-EU countries? Yes No 0
United Kingdom
In case non-EU countries are involved, do the activities undertaken in these countries raise 
potential ethics issues? Yes No
It is planned to use local resources (e.g. animal and/or human tissue samples, genetic material, 
live animals, human remains, materials of historical value, endangered fauna or flora samples,
etc.)? 
Yes No
Is it planned to import any material (other than data) from non-EU countries into the EU or 
from a non-EU country to another non-EU country? For data imports, see section 4. Yes No
Is it planned to export any material (other than data) from the EU to non-EU countries? For 
data exports, see section 4. Yes No
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=== PAGE 31 ===
Administrative forms
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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
Does this activity involve low and/or lower middle income countries, (if yes, detail the benefit-
sharing actions planned in the self-assessment) Yes No
Could the situation in the country put the individuals taking part in the activity at risk? Yes No
7. Environment, Health and Safety Page
Does this activity involve the use of substances or processes that may cause harm to the 
environment, to animals or plants.(during the implementation of the activity or further to the 
use of the results, as a possible impact) ?
Yes No
Does this activity deal with endangered fauna and/or flora / protected areas? Yes No
Does this activity involve the use of substances or processes that may cause harm to humans, 
including  those performing the activity.(during the implementation of the activity or further 
to the use of the results, as a possible impact) ?
Yes No
8. Artificial Intelligence Page
Does this activity involve the development, deployment and/or use of Artificial Intelligence-
based systems? Yes No 0
9. Other Ethics Issues Page
Are there any other ethics issues that should be taken into consideration? Yes No
I confirm that I have taken into account all ethics issues above and that, if any ethics issues apply, I will complete the 
ethics self-assessment as described in the guidelines How to Complete your Ethics Self-Assessment
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=== PAGE 32 ===
Administrative forms
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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
Ethics Self-Assessment
Ethical dimension of the objectives, methodology and likely impact
6. Non-EU Countries 
Will some of the activities be carried out in non-EU countries? 
 
Parts of the research is carried out in UK. The work is of documentary type. 
No impacts and/or misuses on environment, social or political groups are expected.  
 
 
8. Artificial intelligence 
Does this activity involve the development, deployment and/or use of Artificial Intelligence-based systems? 
Yes, first for text generation and second, because the project elaborates on concepts (quantum) machine learning approaches. 
 
Remaining characters 4491
Compliance with ethical principles and relevant legislations
6. Non-EU Countries 
Due to the involvement of UK as non-EU country, the project confirms compliance to the highest ethical standards.  
All research conducted (in either EU Memberstates or UK) will be of legal type in all mentioned countries. 
 
 
8. Artificial intelligence 
QUANTAIR’s actively integrates privacy, safety, fairness, environmental responsibility, dual-use safeguards, and transparency into its 
objectives. Wherever AI is used, it only contributes as a prelimenary step for further verfication. 
 
 
Remaining characters 4492
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Administrative forms
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Proposal ID 101289612
Acronym QUANTAIR
Horizon Europe ver 1.00 20241022 Last saved 17/09/2025 06:50
Security issues table
1. EU Classified Information (EUCI)2 Page
Does this activity involve information and/or materials requiring protection against 
unauthorised disclosure (EUCI)? Yes No
Does this activity involve non-EU countries which need to have access to EUCI? Yes No
2. Misuse Page
Does this activity have the potential for misuse of results? Yes No
3. Other Security Issues Page
Does this activity involve information and/or materials subject to national security restrictions? 
If yes, please specify: (Maximum number of characters allowed: 1000)
Yes No
Are there any other security issues that should be taken into consideration? 
If yes, please specify: (Maximum number of characters allowed: 1000)
Yes No
Security self-assessment
(No Security Issue was marked with Yes)
Remaining characters 4961
2According to the Commission Decision (EU, Euratom) 2015/444 of 13 March 2015 on the security rules for protecting EU classified information, “European Union 
classified information (EUCI) means any information or material designated by an EU security classification, the unauthorised disclosure of which could cause varying 
degrees of prejudice to the interests of the European Union or of one or more of the Member States”. 
3Classified background information is information that is already classified by a country and/or international organisation and/or the EU and is going to be used by the 
project. In this case, the project must have in advance the authorisation from the originator of the classified information, which is the entity (EU institution, EU Member 
State, third state or international organisation) under whose authority the classified information has been generated. 
4EU classified foreground information is information (documents/deliverables/materials) planned to be generated by the project and that needs to be protected from 
unauthorised disclosure. The originator of the EUCI generated by the project is the European Commission.
This proposal version was submitted by Andreas Spörl on 16/09/2025 11:51:52 Brussels Local Time. Issued by the Funding & Tenders Portal Submission System.

=== PAGE 34 ===
Call: [HORIZON-SESAR-2023-DES-ER3] — [Digital European Sky Exploratory Research 03] 
EU Grants: Application form (HE RIA and IA)( HORIZON-SESAR-2025-DES-ER-03): V4.0 – 18.12.2024 
 
 
 
 
Part B - Page 1 of 50 
 
Proposal template Part B: technical description 
QUANTAIR 
[This document is tagged. Do not delete the tags; they are needed for processing.] #@APP-FORM-HERIAIA@# 
List of participants 
Participant No. * Participant organisation name Country 
1 (Coordinator) Deutsches Zentrum für Luft- und Raumfahrt (DLR e.V.) Germany 
2 Qoro Quantum United Kingdom 
3 SkyNav International Belgium 
 
 
1. Excellence  #@REL-EVA-RE@#  
1.1 Objectives and ambition #@PRJ-OBJ-PO@# 
QUANTAIR investigates the feasibility of applying Quantum Federated machine Learning (QFL) to Air Traffic 
Management (ATM) at TRL 1. Federated Machine Learning (FedML) is a distributed approach in which models are 
trained across multiple independent data sources without requiring the exchange of raw data. This is of clear relevance 
to ATM, where operational datasets are fragmented across various stakeholders such as airlines, ANSPs, airports, 
and regulators, and where data protection, confidentiality, and sovereignty considerations prevent centralisation. 
Quantum computing is still at an early stage and cannot yet be used in operational systems. However, research in 
other fields suggests that certain quantum algorithms can eventually provide advantages when dealing with very large 
or complex problems, such as those involving many interdependent variables [1]. FedML, by contrast, is already a 
recognised approach that allows different stakeholders to train models collaboratively without sharing their raw data 
[2]. In QUANTAIR, the focus is on exploring the feasibility of combining FedML with the capabilities of Quantum 
computing for ATM at TRL 1.  
The project aims to provide the first structured assessment of QFL for ATM. Its contribution is not the development 
of prototypes or operational tools, but rather the creation of a conceptual and methodological foundation on which 
future SESAR research can build. QUANTAIR clarifies whether QFL represents a promising research direction for 
ATM, defines the conditions under which it might deliver benefits, estimates quantum resources, and identifies the 
steps required to advance to TRL 2–3. The previous work by EUROCONTROL for the SESAR programme, called 
AICHAIN [3], proved that classical federated learning (FL) can improve ATM predictions such as ETOT and route 
forecasting without requiring sensitive data to be centralised. This showed clear value in applying FedML to 
fragmented, confidential datasets across airline s, ANSPs, and airports. However, classical FedML faces limits as 
ATM problems grow in size and complexity. Multi-flight trajectory optimisation, higher airspace operations, contrail 
modelling, and large -scale disruption management create exponential decisi on spaces and high -dimensional data 
where classical methods struggle. 
QFL offers a pathway beyond these constraints. Quantum machine learning and sampling techniques could improve 
scalability and trainability, speed up convergence, and capture correlations that classical FedML may miss [4]. They 
also enable richer modelling of rare but safety -critical events, strengthening resilience. The benefit is twofold: 
immediate applicability through privacy-preserving FedML, and long-term potential to exploit quantum acceleration 
once hardware matures. This positions QFL as both a natu ral evolution of AICHAIN’s work and a future -proofed 
approach aligned with SESAR’s digital and green ATM priorities [5]. By targeting ATM, the project provides a 

=== PAGE 35 ===
Call: [HORIZON-SESAR-2023-DES-ER3] — [Digital European Sky Exploratory Research 03] 
EU Grants: Application form (HE RIA and IA)( HORIZON-SESAR-2025-DES-ER-03): V4.0 – 18.12.2024 
 
 
 
 
Part B - Page 2 of 50 
 
demanding testbed for QFL. ATM data is fragmented across many stakeholders, highly sensitive, and time-critical, 
which pushes QFL beyond simpler academic demonstrations. Developing the ATM use cases will expose how QFL 
copes with heterogeneous datasets, pr ivacy constraints, and multi -objective optimisation. These lessons will be 
transferable to other sectors that face similar challenges, such as energy grids, healthcare networks, or logistics chains 
[1]. The project also clarifies where quantum methods add value beyond classical FL, for example in scaling federated 
models, simulating rare events, or solving complex optimisation problems. In doing so, it creates benchmarks, 
reference architectures, and research roadmaps that the wider QFL community can build upon. The outputs therefore 
go beyond ATM: they define generic methods, highlight open technical questions, and establish a foundation for 
advancing QFL across domains. 
To achieve the project objectives, the work is organised around four Exploratory Cases, each representing a distinct 
ATM challenge. 
• PREDICT (Privacy-Respecting Enhanced Departure Integration with Coordinated Timing ) focuses 
on departure predictability and slot allocation. Today, flight departure times are subject to frequent 
uncertainty, creating knock -on effects for network efficiency and traffic flow management. The case 
investigates whether federated models train ed across multiple stakeholders could improve A -CDM 
performance in areas such as estimate accuracy (Estimated Take-Off Time (ETOT), Target Take-Off Time 
(TTOT), etc.) and CTOT regula tion application (supporting the SESAR goal of reduced CTOT allocation 
through 4D trajectory management), without requiring the sharing of commercially sensitive data. 
• HORIQON (High-altitude Operations with Real -time Integrated Quantum -Optimised Navigation) 
examines trajectory planning and integration in higher airspace. As new entrants, such as high -altitude 
platforms and space transport operations, begin to interact with conventional aviation, new methods will be 
required to manage trajectories where littl e operational data currently exists. The case explores how QFL 
could enable collaborative modelling of such trajectories in a way that protects sensitive data and supp orts 
safe integration. 
• SHIELD (Strengthened Handling of Irregular Events and Landing Diversions)  addresses diversion 
capacity and network resilience. Disruptions such as weather, technical failures, or security incidents often 
require diversions, but the available diversion capacity across the network is not always visible to all actors. 
This case considers whether QFL could allow stakeholders to model and share diversion capacity information 
without exposing sensitive or commercially confidential data, improving the network’s ability to respond to 
disruptions. 
• CONTRADE (Climate-Optimised Navigation for TRAjectory DEsign) investigates contrail prediction 
to support environmentally optimised planning. Avoiding persistent contrails is recognised as a key measure 
for reducing the climate impact of aviation, but predicting where contrails will form requires combining data 
from multiple sources. The case explores whether QFL could be used to generate contrail risk maps 
collaboratively, improving environmental outcomes while respecting data ownership and privacy. 
PREDICT focuses on operational efficiency and network capacity. HORIQON addresses the integration of new 
entrants such as high-altitude and space transport operations. SHIELD responds to the need for resilience in the face 
of disruption. CONTRADE tackles e nvironmental sustainability by examining contrail avoidance strategies. 
Together, they demonstrate how QFL could be applicable to very different aspects of the ATM system rather than 
concentrating on a single narrow problem. 
Together, these cases ensure that the research addresses operational efficiency, integration of new entrants, resilience, 
and sustainability. They provide representative contexts for assessing QFL concepts at TRL 1, without moving into 
prototyping or operational validation. They were chosen to ensure that results are relevant across the breadth of the 
European ATM Master Plan. 
  

=== PAGE 36 ===
Call: [HORIZON-SESAR-2023-DES-ER3] — [Digital European Sky Exploratory Research 03] 
EU Grants: Application form (HE RIA and IA)( HORIZON-SESAR-2025-DES-ER-03): V4.0 – 18.12.2024 
 
 
 
 
Part B - Page 3 of 50 
 
QUANTAIR sets out to achieve the following objectives: 
1. Investigate whether FedML  is conceptually applicable to ATM problems, while also considering where 
quantum methods might support its scalability. 
• The first objective is to determine the feasibility of applying QFL to representative ATM challenges. 
This involves examining whether distributed learning methods can be adapted to the constraints of 
ATM, such as heterogeneous data sources, privacy restric tions, and operational time -criticality. It 
also considers whether quantum techniques could, in future, play a role in scaling these methods to 
larger or more complex problems. The outcome will be a feasibility analysis supported by conceptual 
models and indicators. 
2. Develop high-level frameworks for QFL in each Exploratory Case. 
• Each of the four cases is chosen to represent a distinct ATM challenge. For each case, the project 
will develop a conceptual framework describing how QFL could be applied, what the learning 
architecture might look like, what kinds of data would be needed, and how results could support 
operational decision-making. These frameworks will be high -level and exploratory, but they will 
provide a structured representation that can be taken forward in subsequent research. 
3. Identify potential benefits, limitations, and open research questions. 
• The third objective is to move beyond purely speculative discussion by systematically assessing both 
advantages and drawbacks. Potential benefits may include improved predictability, resilience, or 
environmental outcomes. Limitations may relate to data qua lity, computational constraints, or 
explainability of the models. Open questions may include how federated models could be validated 
in a safety -critical environment or how quantum methods could be integrated when hardware 
becomes more mature. The identification of such gaps is itself a valuable outcome, as it directs future 
SESAR work towards areas of highest relevance. 
4. Define the steps needed to advance this research to TRL 2–3 in future work. 
• Finally, the project aims to provide a roadmap for subsequent SESAR research. This includes 
identifying the data -sharing frameworks, modelling techniques, and validation approaches that 
would be required for TRL 2–3 studies. It also considers how and when quantum machine learning 
methods might become relevant. This ensures that the project’s outputs are not isolated academic 
results but are directly linked to the SESAR innovation pipeline. 
QUANTAIR responds directly to the scope of HORIZON -SESAR-2025-DES-ER-03-WA1-3 by investigating the 
application of quantum computing methods to ATM. At TRL 1, the emphasis is placed on federated learning 
approaches and their conceptual integration into ATM, while also identifying where quantum machine learning could 
in future support scalability. The objectives are realistic within a TRL 1 framework and are measurable through 
conceptual indicators such as model fidelity, scalability analysis, and qualitative assessment of potential benefits. In 
doing so, the project provides a structured contribution to the SESAR Master Plan’s long-term vision of the Digital 
European Sky and the research priorities set out in the Work Programme. 
In terms of ambition and positioning, QUANTAIR fills a critical research gap. FedML has been explored in limited 
ways within aviation, mostly for localised applications such as airport processes or isolated ANSP datasets. There 
has been no systematic investigation of whether these techniques could be scaled across the network or used in multi-
stakeholder settings where privacy and sovereignty concerns prevent centralised data sharing. Similarly, while 
quantum computing research is advancing rapidly, applications to ATM have not yet been explored. QUANTAIR is 
therefore the first structured attempt to bring all of the previous research initiatives together into a tangible plan for 
development towards future ATM solutions beyond TRL 3. 
The project targets TRL 1 and does not intend to develop prototypes, demonstrators, or operational tools. Its ambition 
lies instead in being the first project to provide a structured assessment of QFL in holistic ATM, grounded in 
representative use cases, and linked to SESAR’s strategic objectives. The outputs will be conceptual frameworks, 
feasibility analyses, and roadmaps that guide the next phase of research. This ensures that the project is both realistic 
in its scope and ambitious in its contribution,  establishing a research direction that could shape European ATM 
innovation for the next decade. 

=== PAGE 37 ===
Call: [HORIZON-SESAR-2023-DES-ER3] — [Digital European Sky Exploratory Research 03] 
EU Grants: Application form (HE RIA and IA)( HORIZON-SESAR-2025-DES-ER-03): V4.0 – 18.12.2024 
 
 
 
 
Part B - Page 4 of 50 
 
#§PRJ-OBJ-PO§# 
1.2 Methodology #@CON-MET-CM@# #@COM-PLE-CP@# 
FedML is a machine-learning approach in which models are trained collaboratively across distributed data sources 
without the need to pool raw data in a central repository. This is relevant to ATM, where operational data is 
fragmented between stakeholders (such as airlines, ANSPs, airports, and regulators) and where concerns over 
privacy, sovereignty, and commercial sensitivity prevent centralisation. By allowing stakeholders to contribute to 
shared models without releasing their raw data, FedML provides a potential way of overcoming these barriers. 
In defining the methodology, QUANTAIR focuses on the use of quantum computing as a conceptual analysis. The 
project is scoped at TRL 1 not due to a lack of capability, but because current quantum hardware is not yet sufficiently 
mature to enable higher -TRL validation in ATM. QUANTAIR therefore focuses on conceptual feasibility while 
identifying the pathways that can be pursued once the technology advances. The research examines how federated 
learning frameworks in ATM could, in principle, benefit from quant um methods for future TRLs, particularly in 
handling large-scale optimisation and high-dimensional learning tasks. This ensures the project remains grounded in 
what is achievable at TRL 1, while establishing a forward-looking research pathway relevant to SESAR’s long-term 
ambitions. 
In QUANTAIR, QFL[DS1] is defined as the use of federated learning to enable distributed model training across 
ATM stakeholders, combined with an explicit recognition of the potential role of quantum methods in future TRLs. 
The research is not intended to deliver operational tools but to establish whether this approach is feasible and useful 
in an ATM context. 
The methodology is based on several assumptions. It assumes that relevant data is distributed across multiple 
stakeholders, that this data varies in quality and format, and that privacy and sovereignty concerns will prevent full 
centralisation. It assumes that local training and global aggregation can be adapted to ATM use cases, and that 
scalability may require quantum-enabled methods in future TRLs. These assumptions frame the scope of the work 
and define the limits of the feasibility analysis. 
This conceptual basis is well suited to the project’s objectives. The aim is not to deliver prototypes but to investigate 
feasibility, develop high-level frameworks, identify potential benefits and limitations, and define pathways towards 
higher-TRL research. FedML provides the structure for exploring distributed collaboration in ATM, and quantum 
computing offers a forward-looking perspective that connects the work to the long-term vision of SESAR. 
FedML is a machine-learning approach in which models are trained collaboratively across distributed data sources 
without the need to pool raw data in a central repository. This is relevant to ATM, where operational data is 
fragmented between stakeholders ( such as airlines, ANSPs, airports, and regulators) and where concerns over 
privacy, sovereignty, and commercial sensitivity prevent centralisation. By allowing stakeholders to contribute to 
shared models without releasing their raw data, FedML provides a potential way of overcoming these barriers. 
In defining the methodology, QUANTAIR focuses on the use of quantum computing as a conceptual analysis. The 
project is scoped at TRL 1 not due to a lack of capability, but because current quantum hardware is not yet sufficiently 
mature to enable higher -TRL validation in ATM. QUANTAIR therefore focuses on conceptual feasibility, while 
identifying the pathways that can be pursued once the technology advances. The research examines how federated 
learning frameworks in ATM could, in principle, benefit from quan tum methods once they become more capable, 
particularly in handling large-scale optimisation and high-dimensional learning tasks. This ensures that the project 
remains grounded in what is achievable at TRL 1, while still establishing a forward-looking research pathway that is 
relevant to SESAR’s long-term ambitions. 
In QUANTAIR, QFL is defined as the use of federated learning to enable distributed model training across ATM 
stakeholders, combined with an explicit recognition of the potential role of quantum methods in overcoming 
computational bottlenecks in the future. The research is  not intended to deliver operational tools but to establish 

=== PAGE 38 ===
Call: [HORIZON-SESAR-2023-DES-ER3] — [Digital European Sky Exploratory Research 03] 
EU Grants: Application form (HE RIA and IA)( HORIZON-SESAR-2025-DES-ER-03): V4.0 – 18.12.2024 
 
 
 
 
Part B - Page 5 of 50 
 
whether this approach is feasible and useful in an ATM context. 
The methodology is based on several assumptions. It assumes that relevant data is distributed across multiple 
stakeholders, that this data varies in quality and format, and that privacy and sovereignty concerns will prevent full 
centralisation. It assumes that local training and global aggregation can be adapted to ATM use cases, and that 
scalability may eventually require quantum-enabled methods. These assumptions frame the scope of the work and 
define the limits of the feasibility analysis. 
This conceptual basis is well suited to the project’s objectives. The aim is not to deliver prototypes but to investigate 
feasibility, develop high-level frameworks, identify potential benefits and limitations, and define pathways towards 
higher-TRL research. FedML provides the structure for exploring distributed collaboration in ATM, and quantum 
computing offers a forward-looking perspective that connects the work to the long-term vision of SESAR.   
Exploratory Case 1: PREDICT (Privacy-Respecting Enhanced Departure Integration with Coordinated 
Timing) 
One of the four exploratory cases is PREDICT, which examines the problem of departure predictability and slot 
allocation. In the current ATM system, uncertainty in the timing of departures is a persistent source of inefficiency. 
Airlines, airports, and the Network Manager rely on estimates for the various trigger points within the flight, which 
are used to consider airspace loading and whether regulation needs to be applied to protect the airspace. This includes 
estimates used within A -CDM, such as Target Off -Blocks Time (TOBT), Target Start Approval Time (TSAT), 
Estimated Take-Off Time (ETOT) etc., which are used to plan the allocation of slots via Calculated Take-Off Time 
(CTOT) and to coordinate traffic flows, but these estimates are often unreliable and have a tolerance window applied. 
Delays caused by late boarding, pushback, or ground handling propagate through th e system, leading to inefficient 
use of airport capacity and sub -optimal flow management decisions. Improving departure predictability has been a 


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long-standing goal in SESAR, but progress has been constrained by the limitations of centralised modelling 
approaches. 
The challenge is that the information required to predict departures accurately is distributed across multiple actors. 
Airlines hold data on crew, passengers, and turnaround processes. Airports manage gate availability, ground 
handling, and sequencing. Air  navigation service providers track surface movements and manage departure 
clearances. The network manager  needs a system-wide picture in order to coordinate slots at a regional level. At 
present, much of this information is kept within individual organisations, either because it is operationally sensitive 
or because there are no mechanisms to share it in real time. As a result, centralised models rely on incomplete data, 
and their predictions lack the accuracy needed to deliver significant improvements. 
The PREDICT case investigates whether FedML could provide a more effective framework. In a federated approach, 
each stakeholder would train a local model on its own data, and only model parameters would be shared. A global 
model could then be constructed t hat reflects the behaviour of the entire system without requiring the exchange of 
raw data. For example, an airline’s local model could capture patterns in boarding times, while another airport’s 
model could capture ground handling delays. The aggregated m odel could then produce more reliable A -CDM 
estimates, which in turn could improve slot allocation and flow management decisions. 
At TRL 1, the project does not attempt to build such models but rather to examine their conceptual feasibility. The 
methodology involves defining a high -level framework for how federated models might be constructed in this 
context, identifying what types of data would be needed at each node, and analysing the assumptions required for the 
models to function. Indicators such as potential improvements in model fidelity, the robustness of aggregation across 
heterogeneous datasets, and the scalability of the app roach will be considered. The analysis will also examine the 
limitations of FedML in this application, such as the risk of bias if data quality differs significantly between 
stakeholders or the difficulty of validating models in a safety-critical environment. 
The role of quantum computing is considered solely as a conceptual pathway for future TRLs. With more accurate 
ETOT predictions produced by federated models, the subsequent task of sequencing departure slots becomes a 
high‑dimensional, NP‑hard scheduling problem in which classical methods are known to struggle. 
Here, the federated setup is essential: only by combining distributed insights from airlines, airports, and network 
managers can a sufficiently rich optimisation space be created for downstream quantum‑machine‑learning algorithms 
to act upon. The project w ill evaluate how quantum techniques, particularly hybrid quantum‑classical approaches 
such as the Quantum Approximate Optimization Algorithm (QAOA), variational quantum circuits, and 
quantum‑annealing‑based methods, could be applied at this global level. F or example, quantum‑machine‑learning 
models, seeded with federated‑learning‑derived estimates, could generate high‑quality slot sequences that minimise 
delay propagation. 
Furthermore, we will investigate whether quantum computers can effectively evaluate the probability of rare but 
critical events, such as sector congestion, enabling unsafe or inefficient allocations to be rejected without exhaustive 
simulation. Hybrid workflows will be designed so that classical pre‑processing (e.g., feature extraction, constraint 
filtering) feeds into the quantum optimiser, while classical post‑processing (e.g., solution refinement, robustness 
checks) cleans and validates the quantum output. 
These techniques illustrate how federated learning provides the conceptual foundation for future quantum integration, 
while hybrid quantum‑classical methods offer a practical bridge between current capabilities and the eventual 
deployment of fully quantum‑enhanced scheduling solutions. 
The expected output of the PREDICT case is therefore twofold: first, a conceptual framework showing how federated 
learning can provide a privacy-preserving yet system-wide approach to departure predictability and slot allocation; 
and second, a roadmap outlining how quantum machine learning could build on this federated foundation in future 
TRL 2–3 work. By emphasising that accurate, system-wide models can only emerge in a federated setting, the case 
highlights that quantum methods are not an alternative to data sharing but a complement that relies on federated 

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inputs to be effective. This dual perspective will clarify both the feasibility and the limitations of QFL, and determine 
whether distributed learning combined with quantum acceleration offers a credible path toward improved A -CDM 
accuracy, operational efficiency, and capacity management. 
 
Exploratory Case 2: HORIQON (High-altitude Operations with Real-time Integrated Quantum-Optimised 
Navigation) 
HORIQON addresses trajectory planning and integration in higher airspace. New entrants such as high -altitude 
platforms, sub-orbital vehicles, supersonic and hypersonic aircraft, space transport systems, and certain military 
operations create demands that d iffer significantly from conventional traffic. These vehicles operate with unique 
performance profiles, often cross through controlled airspace at high speed, and interact with en-route flows at non-
standard altitudes. Current trajectory planning methods a re not well adapted to these conditions, and the lack of 
operational data makes it difficult to develop predictive models. HORIQON investigates whether QFL could provide 
a framework for sharing knowledge across stakeholders, enabling collaborative modellin g of higher -airspace and 
high-speed trajectories while protecting sensitive operational and security-related data. 
The main difficulty is that responsibility for higher airspace operations is spread across multiple organisations. All 
stakeholders hold sensitive operational data, individual states maintain oversight of safety and sovereignty, and 
ANSPs must manage interactions with conventional traffic within current controlled airspace. Sharing of information 
is complex due to national security considerations, the commercial confidentiality of operational profiles, and 
financial sensitivities. As a result, efforts to model higher airspace trajectories are often fragmented, and the lack of 
integrated predictive capability makes it difficult to anticipate how such operations will affect the wider network. 
The HORIQON case examines whether QFL could provide a way of combining insights from distributed datasets 
without requiring disclosure of sensitive information. A federated model could allow the various stakeholders to train 
local models on their own data while contributing to a global model that captures trajectory behaviours in higher 
airspace. For example, space operators could train models on ascent and descent profiles, while ANSPs could 
contribute models on interactions with controlled airspace. Aggre gating these models could improve the ability to 
predict trajectories and their impact on the network, without requiring operators to reveal sensitive operational details. 
At TRL 1, the focus is on developing a conceptual framework for how such federated models might be constructed 
and on identifying the assumptions needed for them to function. The feasibility analysis will consider factors such as 
the extreme heterogeneity of data sources, the sparsity of available datasets, and the sensitivity of mission 
information. Indicators of feasibility will include the ability of federated models to generalise from limited local data, 
the robustness of aggregation when nodes have ver y different information, and the potential scalability of the 
approach. 
The role of quantum computing is considered solely as a conceptual pathway for future TRLs. High -altitude 4D 
trajectory deconfliction requires balancing safety, mission objectives, restricted zones, and sovereignty constraints 
across multiple flight inform ation regions. This rapidly becomes a computationally hard problem, where classical 
methods are unlikely to scale. A federated setup is indispensable since no single operator or authority can or will 
provide the data diversity required to model high-altitude trajectories; only by linking their local models can a realistic 
system-wide view emerge. On this federated foundation, quantum methods could potentially be applied in future 
TRLs.  
We will analyse if quantum machine learning algorithms and hybrid approaches are able to provide scalable ansatzes 
to explore the large space of possible solutions, thus converging more rapidly to high -quality, conflict-free routes 
than classical only approaches may allow. Quantum enhanced algorithms could evaluate rare-event probabilities such 
as high-altitude conflict likelihood or space-weather-induced route degradation, leveraging distributed risk models 
trained by operators. Quantum kernel methods are able to classify trajectory feasibility under multiple constraints, 
refining the features provided. 

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While quantum computing offers significant benefits for high -altitude 4D trajectory deconfliction, it is essential to 
consider the potential obstacles that may arise during implementation, such as hardware limitations, regulatory 
obstacles, and difficultie s in integrating quantum algorithms with existing classical systems. Addressing these 
concerns will help ensure that the proposed solution is feasible and effective in practice. 
The project will analyse the basic application principles of appropriate hybrid quantum algorithms in ATM according 
to TRL 1. 
Higher airspace, generally above flight level 550, is at present largely unused by commercial aviation. This relative 
vacancy offers a rare opportunity to introduce new technologies and working methods without the immediate 
constraints of dense traffic. By treating this environment as a testing ground, ATM can explore innovative approaches 
to integration, coordination, and trajectory management more rapidly than in lower airspace, where changes are 
slowed by operational complexity and legacy systems. 
The expected output of the HORIQON case is therefore twofold: first, a conceptual framework for using federated 
learning to support collaborative trajectory modelling in higher airspace; and second, a roadmap identifying where 
quantum machine learning, and classification techniques could naturally extend this framework in later TRLs. By 
making the federated approach explicit as the necessary foundation, the case highlights that quantum computing is 
not a substitute for collaboration, but a complement that depends on federated inputs to be effective. 
Exploratory Case 3: SHIELD (Strengthened Handling of Irregular Events and Landing Diversions) 
SHIELD addresses the challenge of diversion capacity and network resilience. Air traffic management must be able 
to cope with unexpected events such as severe weather, technical failures, medical emergencies, or security incidents. 
When diversions occur, the availability of suitable airports and the capacity of surrounding airspace become critical 
factors. At present, diversion planning is often managed locally, with each airline or ANSP focusing on its own area 
of responsibility. The wider network view is limited, and information on diversion capacity is not always shared 
across stakeholders. This leads to situations where capacity is underused in some areas while others face excessive 
pressure, reducing the resilience of the system as a whole. 
A key difficulty is that the data required to build a comprehensive picture of diversion capacity is distributed and 
often sensitive. Airlines hold data on operational priorities and alternate airport preferences. Airports manage runway 
and parking availab ility, ground handling resources, and emergency procedures. ANSPs control the surrounding 
airspace and determine how many diversions can be safely accommodated. The Network Manager is responsible for 
regional coordination but does not always have access to timely or detailed information from all stakeholders. At a 
broader level, the European Aviation Crisis Coordination Cell (EACCC) is tasked with coordinating responses to 
major disruptions, but its effectiveness depends on the availability of accurate and up-to-date information from across 
the network. This fragmentation makes it difficult to anticipate the network-wide impact of diversions or to allocate 
resources effectively during disruptions. 
The SHIELD case explores whether federated learning could support more effective modelling of diversion capacity. 
In this approach, each stakeholder would train a local model on its own data, capturing patterns such as airport 
throughput under different we ather conditions or airline diversion preferences. These models could then be 
aggregated to create a broader picture of diversion capacity at the network level, without requiring any actor to release 
its raw operational data. Such a framework could improve  situational awareness and decision -making during 
disruptive events, helping the system to absorb shocks more effectively. 
At TRL 1, the work does not involve implementing such models but rather examining their feasibility. The analysis 
will define a conceptual framework showing how QFL might be applied to diversion capacity modelling. It will 
consider assumptions such as the availability of local data, the variability of conditions across airports, and the need 
for near-real-time aggregation during an unfolding disruption. Feasibility will be assessed using indicators such as 
whether federated approaches can capture the divers ity of conditions across the network, how robust aggregated 
models would be to incomplete or delayed inputs, and what kinds of benefits might arise in terms of resilience. 

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The role of quantum computing is considered solely as a conceptual pathway for future TRLs. Diversion planning 
requires balancing multiple constraints (such as fuel states, alternate readiness, sector loads, and weather) while 
ensuring safety and minimising delay. This quickly becomes a largescale combinatorial optimisation problem, 
especially during cascading disruptions where time pressure is critical. For diversion capacity, federation is crucial: 
airlines, airports, ANSPs, and the Network Manager each control different pieces of the puzzle, and only their 
combined local models can reveal the network’s true resilience during disruptions. 
On this federated foundation, quantum techniques could potentially be applied in future TRLs. Kernelbased quantum 
anomaly detection could identify unusual diversion patterns from historical disruptions, enabling early recognition 
of bottlenecks in evolving crises. Quantum random walks could explore the connectivity graph of airports and sectors, 
rapidly highlighting underused alternates and feasible redirection chains. Quantum algorithms for probability 
estimation could assess the likelihood of alternate ov erload or excessive airborne holding for candidate diversion 
plans, improving safety assessments without the need for extensive classical Monte Carlo runs. 
Hybrid quantumclassical methods (such as variational quantum circuits), the Quantum Approximate Optimization 
Algorithm (QAOA), and quantumannealing based solvers will be evaluated for the diversion allocation problem 
itself. These algorithms can encode the  allocation task, producing high -quality allocations that minimise overload 
and delay even with lastminute changes, while classical pre and postprocessing handles data preparation and result 
validation. 
The project will analyse the basic application principles of appropriate hybrid quantum algorithms in ATM according 
to TRL 1. 
The expected output of the SHIELD case is a feasibility assessment of federated learning for diversion capacity 
modelling, coupled with a forward -looking roadmap showing how quantum optimisation, anomaly detection, and 
probability estimation could reinforce network resilience under disruption. By making the federated approach explicit 
as the foundation, the case highlights that quantum computing is not an alternative to collaboration but a complement 
that depends on federated inputs. In doing so, SHIELD wil l clarify whether distributed approaches can improve 
visibility and coordination during crises, supporting the long-term goal of a more resilient and robust European ATM 
system. 
Exploratory Case 4: CONTRADE (Climate-Optimised Navigation for TRAjectory DEsign) 
CONTRADE investigates contrail prediction to support environmentally optimised planning. Persistent contrails and 
the cirrus clouds they can form are recognised as a significant contributor to the climate impact of aviation. Avoiding 
contrail-forming regions can therefore reduce the environmental footprint of flights, but predicting where contrails 
will occur is technically challenging. Accurate prediction maps require the integration of meteorological data, 
complex atmospheric conditions, aircraft performance characteristics, and traffic patterns. At present, this integration 
is limited, and decision-making relies on simplified models that cannot capture the full variability of the prevailing 
atmospheric conditions. 
The challenge lies in the distributed nature of the relevant data. Meteorological agencies produce forecasts of 
temperature, humidity, and wind fields at multiple altitudes. Airlines hold data on aircraft performance and 
operational routing preferences, in cluding cost-index settings that determine whether the airline prioritises speed, 
fuel efficiency, or other operational factors. This information is highly sensitive, as it reflects commercial strategies 
and fuel-burn targets. ANSPs manage the airspace structure and flow constraints that determine whether avoidance 
trajectories are feasible. In addition, contrail mitigation itself involves a complex trade-off: avoiding contrail-forming 
regions may require flying at less efficient altitudes, leading to highe r fuel burn and associated harmful emissions. 
Each actor therefore possesses part of the information needed, but there is no mechanism for combining it into shared 
contrail-risk maps without significant data exchange. Concerns over intellectual property, operational sensitivity, and 
the lack of common standards further hinder centralised approaches. 

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The CONTRADE case investigates whether federated learning could provide a framework for collaborative contrail-
risk modelling. In this approach, each stakeholder would train local models that capture how their data contributes to 
contrail formation. Meteorological agencies could model atmospheric conditions, while aircraft manufacturers could 
contribute data on performance characteristics and contrail sensitivity of specific airframes and engines. Airlines 
would provide models based on their operational use of these aircraft, including cost-index preferences that determine 
whether flights are optimised for speed, fuel efficiency, or specific fuel-burn targets. ANSPs could contribute models 
that reflect operational feasibility in terms of sector capacity and airspace restrictions. Aggregating these models 
could produce risk maps that are richer and more accurate than those generated by any single actor, without requiring 
raw data to be shared. 
At TRL 1, the work is limited to conceptual analysis. The methodology involves defining the types of local models 
that might be trained, the assumptions needed for aggregation, and the indicators that could be used to judge 
feasibility. These indicators in clude the potential improvement in the resolution and reliability of risk maps, the 
robustness of results when datasets are incomplete or inconsistent, and the scalability of the approach to larger 
geographic regions. The analysis will also consider limitations, such as the challenge of validating contrail predictions 
against observed outcomes and the computational demands of processing large volumes of atmospheric data. 
The role of quantum computing is considered a potential future enabler. Contrail avoidance routing is inherently a 
multi-objective optimisation problem: flights must minimise climate forcing, fuel burn, and congestion 
simultaneously. Classical methods struggle as the number of route/altitude combinations grows. 
Here, the federated setup is essential: only by combining local models from meteorological agencies, aircraft 
manufacturers, airlines, and ANSPs can the diverse data needed for contrail prediction be assembled without 
compromising sensitive inputs. 
On this federated foundation, quantum methods could extend capability. Hybrid quantumclassical optimisation (such 
as variational quantum circuits or the Quantum Approximate Optimization Algorithm (QAOA)) could evaluate many 
potential route –altitude combina tions more efficiently, enabling targeted contrail avoidance without degrading 
overall network performance. Quantum algorithms for probability estimation could assess the chance of contrail 
persistence along candidate routes using aggregated weather performance models, reducing the need for largescale 
classical sampling. Finally, quantum kernels could project aggregated weather performance data into high -
dimensional feature spaces where persistent contrail conditions become linearly separable, improving the precision 
of contrail riskmaps. 
The expected outcome of the CONTRADE case is to demonstrate whether federated learning can provide a practical 
basis for collaborative contrail -risk mapping, and to highlight where quantum methods could eventually enhance 
such models. Rather than positioning quantum as a separate solution, the analysis will explore how machine learning, 
estimation, and classification techniques can be layered on top of federated inputs to deliver more accurate and 
actionable risk maps. By extending QFL into the environmenta l domain, CONTRADE broadens the project’s 
relevance: alongside efficiency, integration, and resilience, it directly supports SESAR’s strategic objective of 
reducing aviation’s climate impact and making Europe the most environmentally sustainable region to fly. 
A Coordinated approach 
Taken together, the four exploratory cases ensure that the methodology is comprehensive and representative of the 
challenges facing European ATM. PREDICT addresses operational efficiency in day -to-day traffic management, 
HORIQON looks ahead to the integration of new entrants in higher airspace, SHIELD focuses on network resilience 
under disruption, and CONTRADE examines environmental sustainability. By covering efficiency, integration, 
resilience, and environment, the project avoids concentrating on a single niche problem and instead demonstrates 
how Quantum Federated Learning could, in principle, be applied across the full scope of the European ATM Master 
Plan. This breadth ensures that the feasibility analysis is robust, highlights both common and domain -specific 
challenges, and maximises the value of the results for guiding future SESAR research. 

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Transversal cross-cutting 
The four exploratory cases provide the primary structure for the research, but several cross -cutting considerations 
influence the methodology as a whole. These reflect the interdisciplinary nature of the work, the constraints under 
which the research must operate, and the principles that ensure it is consistent with European policy goals. 
The project requires expertise from multiple domains. Air traffic management provides the operational context, 
defining the problems that need to be addressed and the performance criteria against which solutions must be judged. 
Machine learning contributes the methods for distributed model training, while quantum research provides the longer-
term perspective on scalability and algorithmic possibilities. Regulatory and policy insight ensures that the proposed 
frameworks remain realistic in the institutional environment of European aviation. The combination of these 
perspectives is essential; no single discipline is capable of addressing the research questions in isolation. The 
methodology is therefore deliberately interdisciplinary, ensuring that technical fe asibility is always considered in 
relation to operational relevance and policy constraints. 
The assumptions underpinning the methodology are also common across the exploratory cases. These include the 
expectation that operational data will remain fragmented across multiple stakeholders, that privacy and sovereignty 
concerns will prevent full centralisation, and that datasets will be heterogeneous in both quality and structure. The 
feasibility analyses therefore focus on whether federated learning can function under these conditions rather than 
assuming idealised data availability. A further assumption is that validation of distributed models in a safety-critical 
domain will pose challenges that cannot be fully resolved at TRL 1. Instead, the project aims to identify those 
challenges explicitly, providing a roadmap for how they could be addressed in higher-TRL work. 
The research is conducted in accordance with the “do no significant harm” principle of the EU Taxonomy Regulation. 
The work involves only conceptual frameworks and analyses, and does not generate environmental impacts directly. 
Its contribution is instead positive, particularly in the CONTRADE case, where the methodology is explicitly directed 
towards reducing the climate impact of aviation by enabling contrail avoidance strategies. The project also contributes 
indirectly to sustainability by exploring meth ods that may improve efficiency and resilience, thereby reducing 
unnecessary fuel burn. 
Because the project involves the application of machine learning concepts, attention is given to the robustness and 
explainability of such methods. Federated learning introduces specific risks, including the potential for bias if local 
datasets are unbalanced and the difficulty of validating aggregated models when raw data is not accessible. These 
issues will be examined systematically in the feasibility analyses, and potential safeguards will be identified. The 
project anticipates that any future integration of quantum methods will require similar attention to transparency and 
verification, particularly in a safety-critical environment. The project therefore aligns with European principles on 
trustworthy AI by ensuring that robustness, reproducibility, and explainability are considered from the outset, even 
at TRL 1. 
In summary, the methodology integrates expertise from multiple disciplines, is grounded in explicit assumptions 
about the ATM environment, complies with European sustainability principles, and recognises the importance of 
robustness and explainability in AI methods. These cross-cutting considerations ensure that the exploratory analyses 
are not only technically sound but also aligned with the broader requirements of the aviation system and the European 
research framework. 
Open Scientific Principles and Responsible Research 
The methodology also incorporates principles of open science and responsible research management. Although the 
project is exploratory and limited to TRL 1, the outputs include conceptual frameworks, feasibility assessments, and 
analytical results that can be openly shared. Publications will be made available in open -access journals or 
repositories in line with Horizon Europe requirements. Preprints and working papers will be released where 
appropriate to support early dissemination and peer feedback. Models and analytical frameworks developed during 

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the project will be documented and, where possible, shared in a way that enables scrutiny and reuse by the wider 
research community. 
Research data management follows the FAIR principles (Findable, Accessible, Interoperable, Reusable). The project 
will not generate new operational datasets from its activities. Instead, it will make use of modest datasets that may 
include synthetic data c reated for illustrative purposes, publicly available sources such as EUROCONTROL 
performance statistics or open meteorological data, and structured documentation of modelling assumptions. These 
materials will be curated with clear documentation of provenance, scope, and format. A data management plan will 
be produced by month six   of the project and updated as required. Persistent identifiers will be used to ensure 
findability, and trusted repositories will be selected for long -term preservation. Access will be granted in line with 
open-access requirements, while any data subject t o restrictions for confidentiality or security will be documented 
with clear justification. Reusability will be supported by applying open licences where appropriate and by providing 
explanatory documentation of models and algorithms. 
The project recognises that the integration of a gender dimension in research content is mandatory under Horizon 
Europe unless clearly irrelevant. In the case of QUANTAIR, the subject matter involves the conceptual exploration 
of federated learning and qua ntum computing in ATM. The scientific and operational questions addressed are not 
influenced by biological sex or social gender factors, and the research outputs do not have a differentiated impact 
along those lines. For this reason, the gender dimension is not directly relevant to the research content. However, the 
project will ensure that dissemination and communication activities are inclusive, using language and imagery that 
reflect diversity, and ensuring that engagement with stakeholders and end users is representative of the community. 
These measures ensure that QUANTAIR is aligned with the principles of open and responsible science. The outputs 
will be accessible to the wider research community, the data will be managed transparently, and inclusivity will be 
ensured in dissemination and communication. Even at TRL 1, these practices increase the value of the research by 
making it reproducible, transparent, and usable as a foundation for future SESAR work. 
Leveraging National and International R&I Activities 
The proposed research builds directly upon major European R&I activities and SESAR strategic priorities, ensuring 
continuity and maximising return on investment from prior programmes. One important foundation is AI-CHAIN 
(EUROCONTROL, 2022–2024), which validated the use of collaborative machine learning on distributed, privacy-
sensitive ATM datasets, including Estimated Take-Off Times (ETOT) and trajectory prediction. AI-CHAIN 
showed that federated models could deliver improved accuracy without requiring raw data sharing. QUANTAIR 
will extend these results by exploring Quantum Federated Learning (QFL) as a means to address the scalability and 
machine learning challenges identified in AI-CHAIN. This will be achieved by reusing AI-CHAIN benchmarks, 
adapting its federated architectures, and maintaining close contact with EUROCONTROL stakeholders to 
guarantee methodological alignment. 
The SESAR Master Plan 2025 provides further direction. It explicitly recognises that “machine learning is used in 
combination with conventional deterministic algorithms for trajectory prediction, ATFM and ATC conflict 
detection” and calls for the development of AI capabilities enabling the next generation of ATM platforms. It also 
underlines the importance of “virtualisation and cyber-secure data sharing” and identifies “AI for aviation” as an 
industrial research priority. Furthermore, the Master Plan stresses the need for ATM modernisation to shift towards 
“a modern, data-driven and cloud-based service-oriented architecture,” facilitating privacy-preserving and 
interoperable collaboration across stakeholders. QUANTAIR responds to these calls by investigating QFL as a 
privacy-preserving AI mechanism to overcome sovereignty and sensitivity barriers that prevent centralised data 
sharing, aligning its exploratory cases (departure predictability, higher airspace integration, diversion capacity, and 
contrail avoidance) with SESAR Strategic Deployment Objectives, notably SDO 3 on Dynamic Airspace 
Configuration, SDO 5 on the Transformation to Trajectory-Based Operations (TBO), and SDO 8 on Service-
Oriented Delivery Models. In doing so, the project contributes to SESAR’s long-term roll-out objectives, directly 
addressing the shift towards SDO 5 for precise 4D trajectory optimisation, enabling resilience and scalability in line 
with SDO 3, and supporting the transition to SDO 8. It also adds value to SESAR’s automation and AI research 

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stream, particularly in the areas of high-dimensional optimisation, safety-critical validation, and cross-stakeholder 
model aggregation. 
Beyond SESAR, QUANTAIR will connect to the broader European and international R&I ecosystem. It will 
ensure consistency with Digital Sky Demonstrators, align its environmental work on contrail avoidance and 
climate-optimised trajectories with the Clean Aviation and Zero-Emission Aircraft programmes, and take into 
account international initiatives such as ICAO, FAA NextGen, and NASA ATM-X to ensure interoperability and 
standardisation. 
To embed its results in the SESAR ecosystem, QUANTAIR will feed its conceptual frameworks and feasibility 
analyses into SESAR repositories and knowledge-sharing channels, organise technical workshops with AI-CHAIN 
and SESAR 3 JU expert groups to align assumptions and validation methods, and publish results in open-access 
venues while maintaining strong links with SESAR Innovation Days and technical consultation fora. In this way, 
the project will not duplicate previous work but rather extend and complement SESAR’s federated learning 
strategy, anchoring its results in the Master Plan 2025 vision of a Digital European Sky enabled by AI, privacy-
preserving data sharing, and service-oriented architectures. 
Interdisciplinary Integration of Expertise 
The consortium combines three complementary disciplines essential to the project: quantum algorithms, distributed 
orchestration, and operational ATM. DLR QSOC contributes expertise in quantum computing, quantum machine 
learning and optimisation, leading the scientific feasibility assessment of applying quantum methods to federated 
learning. Qoro Quantum brings knowledge of distributed systems and orchestration, ensuring that the frameworks 
are technically realistic, privacy-preserving, and aligned with SESAR’s transition to service-oriented architectures. 
SkyNav International adds operational ATM and navigation expertise, embedding safety, regulatory, and 
interoperability requirements into the exploratory cases so that the results reflect real-world constraints and 
stakeholder needs. 
Integration will be achieved through co-design workshops, joint development of conceptual frameworks, and 
systematic cross-review of results. DLR will validate the scientific soundness of the approaches, Qoro will ensure 
architectural robustness and scalability, and SkyNav will test operational relevance against SESAR’s Strategic 
Deployment Objectives. This structure ensures that the project outputs are not isolated academic exercises but 
practical and credible contributions to SESAR’s research pipeline, providing actionable pathways for future work 
on Quantum Federated Learning in ATM. 
The combined skill set is both unique and directly relevant to the project’s aims. Quantum algorithm research alone 
cannot address operational feasibility; distributed orchestration expertise alone cannot resolve ATM privacy and 
sovereignty barriers; and operational ATM expertise alone cannot scale to emerging computational methods. By 
uniting these three perspectives, the consortium can evaluate Quantum Federated Learning in a way no single 
partner could achieve in isolation. This integration ensures that the project bridges the gap between theoretical 
research, technical system design, and operational ATM practice, delivering results that are both innovative and 
usable within the SESAR framework. 
Role of Social Sciences and Humanities (SSH) 
The proposed work does not directly require integration of Social Sciences and Humanities (SSH) disciplines, as 
the focus is on the conceptual feasibility of Quantum Federated Learning for Air Traffic Management at TRL 1. 
The research is primarily technical, combining quantum algorithmics, distributed computing architectures, and 
operational ATM knowledge. Its outputs will be high-level frameworks, feasibility analyses, and pathways for 
future research rather than social or behavioural studies. 
That said, the consortium recognises that ATM modernisation is a socio-technical transformation. The SESAR 
Master Plan 2025 explicitly highlights the evolving role of human operators in human–machine teaming and the 

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need for trustworthy AI-based systems to ensure adoption in safety-critical contexts. These aspects fall outside the 
TRL 1 scope of QUANTAIR but are highly relevant to subsequent SESAR research. The project will therefore 
remain attentive to SSH findings from related SESAR activities on data governance, human–machine trust, and 
stakeholder acceptance, and ensure that its conceptual frameworks are compatible with future interdisciplinary 
studies. In this way, while SSH integration is not required for the current exploratory work, the project establishes a 
technical foundation that can be enriched by SSH contributions in later phases. 
Research Data Management and Research Outputs 
QUANTAIR is scoped at TRL 1 and will not generate large operational datasets. The main outputs will be 
conceptual frameworks, feasibility analyses, modelling artefacts, and small synthetic datasets used to test federated 
learning and quantum machine learning concepts. Outputs will be textual (reports, frameworks), numerical 
(synthetic trajectory/weather data), and software-based (lightweight code templates), with total volume <10 GB. 
Where public datasets (e.g. Eurocontrol performance reports, meteorological data) are combined with synthetic 
data, provenance will be documented. 
Findability. All outputs will be assigned persistent identifiers (DOIs) via trusted repositories. Metadata will follow 
Horizon Europe requirements to ensure indexing and searchability. 
Accessibility. Deliverables and publications will be open access. Software and modelling artefacts will be hosted on 
GitHub. Sensitive information, if any, will be restricted but metadata will remain visible for verification. 
Importantly, in line with the federated learning approach, only trained model weights and parameters will be 
shared, never raw stakeholder data. 
Interoperability. Data and code will be provided in non-proprietary formats (CSV, JSON, Python notebooks). 
Metadata will use recognised standards (e.g. Dublin Core) and SESAR taxonomies to ensure compatibility with 
other ATM projects. 
Reusability. Outputs will carry open licenses (CC-BY for documents, Apache 2.0 or MIT for code) to enable reuse 
and integration into future SESAR projects. Documentation and environment files will accompany software to 
support reproducibility. 
Curation and Storage. DLR will curate scientific data and models, Qoro will maintain software repositories, and 
SkyNav will validate operational relevance. Outputs will be preserved in institutional repositories for at least ten 
years. A Data Management Plan will be delivered by Month 6 and updated before project close. 
This approach ensures compliance with FAIR principles and makes results directly reusable by the SESAR 
community in follow-on TRL 2–3 activities. 
 
#§CON-MET-CM§# #§COM-PLE-CP§# #§REL-EVA-RE§#  
2. Impact #@IMP-ACT-IA@# 
2.1 Project’s pathways towards impact 
The results of QUANTAIR are expected to make a meaningful contribution to the SESAR programme’s medium -
term outcomes and to the wider long -term impacts described in the Work Programme. The project is positioned at 
TRL 1, and its immediate outputs are conceptual frameworks and feasibility assessments. The impact therefore lies 
not in the delivery of deployable systems, but in shaping the research agenda by establishing whether Quantum 
Federated Learning (QFL) provides a credible new approach for ATM. By clarifying the potential scope, identifying 

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enabling conditions, and defining pathways to higher-TRL research, the project provides the knowledge base required 
for Europe to maintain leadership in this field, both in federated approaches to ATM and in the application of 
emerging quantum methods for large-scale optimisation and risk assessment. 
The pathways to impact extend across four domains that correspond to the project’s exploratory cases. Each case 
addresses a distinct challenge within the ATM Master Plan, ensuring that the project’s contribution is broad and 
relevant. 
The PREDICT case supports efficiency and capacity by addressing the challenge of departure predictability and slot 
allocation. Delays at departure propagate through the network, and current models are limited because the relevant 
information is fragmented across stakeholders. By investigating whether QFL could enable collaborative prediction 
without compromising data privacy, the project contributes to the SESAR objective of improving predictability as a 
foundation for advanced demand–capacity balancing. Even at TRL 1, the project identifies how federated approaches 
might reduce uncertainty in departure planning and sets out pathways for higher-TRL research that could ultimately 
improve the efficiency of European airspace. 
The HORIQON case supports the safe integration of new entrants in higher airspace. The growing presence of high-
altitude platforms, sub -orbital flights, super - and hyper-sonic vehicles, and space transport operations introduces 
uncertainty into ATM, and the lack of shared data across stakeholders makes it difficult to model these trajectories. 
QUANTAIR explores whether QFL could provide a framework for collaborative trajectory modelling under strict 
confidentiality constraints. This aligns with SESAR’s objective of ensuring the seamless integration of all airspace 
users and supports the EASA Higher Airspace Operations roadmap. It also complements ICAO’s ongoing work in 
the ATMOPS Panel, ATMRPP, and ICAO Paris, where the need for new coordination mechanisms for high-altitude 
operations has been identified. The project’s outputs provide a research basis that can inform both European and 
global efforts in this domain. 
The SHIELD case contributes to resilience and contingency management by addressing diversion handling and rapid 
network reorganisation. Current approaches are fragmented, with limited visibility of network-wide capacity during 
disruptions. By examining whe ther QFL could enable distributed modelling of diversion capacity, the project 
explores how resilience could be enhanced without requiring stakeholders to share sensitive operational data. This 
directly supports SESAR’s long-term ambition of building a more resilient Digital European Sky. It also aligns with 
ICAO’s GATMOC, which emphasises the need for globally harmonised contingency procedures and collaborative 
decision-making. By identifying how distributed modelling could underpin these objectives, QUANT AIR 
contributes to the policy and research foundations required to manage disruptions more effectively at both regional 
and global levels. 
The CONTRADE case contributes to environmental sustainability by considering contrail prediction mapping. 
Persistent contrails represent one of aviation’s most significant climate impacts, but predicting them requires 
combining data from meteorological agencies, airlines, and ANSPs. QUANTAIR explores whether QFL could be 
used to generate richer and more reliable contrail prediction maps without requiring raw data exchange. This supports 
SESAR’s environmental pillar, directly addresses the Horizon Europe priority of making Europe the most 
environmentally friendly region to fly, and complements wider European climate objectives. It also aligns with 
ICAO’s sustainability agenda by exploring methods to reduce the non -CO₂ impacts of aviation. By identifying 
pathways for integrating distributed contrail modelling into ATM processes, the project sets the stage for future 
research that could enable environmentally optimised trajectory planning. 
Across these domains, the project contributes to the realisation of the Digital European Sky vision. QFL represents 
a novel way of enabling scalable services supported by a digital ecosystem, where distributed data sources can 
contribute to shared models w ithout compromising sovereignty or security. This directly addresses one of the 
structural challenges of the Digital European Sky: how to build a networked architecture that is both data -rich and 
privacy-preserving. While QUANTAIR does not deliver technica l systems, it identifies how distributed and 
potentially quantum -enabled approaches could contribute to this architecture in the longer term, by combining 
federated models with quantum machine learning, estimation, and classification methods capable of han dling the 

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scale and complexity of future ATM services. 
The expected impacts also extend beyond SESAR. By addressing data-sharing challenges in ATM through QFL, the 
project informs EASA’s regulatory strategies for higher airspace, ICAO’s work on contingency and environmental 
management, and the global framework established by the GATMOC. It provides European stakeholders with early 
insight into how quantum methods might eventually support large -scale, secure, and collaborative ATM services, 
strengthening Europe’s position as a leader in both ATM modernisation and quantum research. 
The scale and significance of these impacts must be interpreted in the context of TRL 1. The project does not produce 
immediate operational benefits, but it does set the agenda for higher -TRL work that could. Its contribution lies in 
providing the first st ructured assessment of QFL in ATM, across efficiency, new entrants, resilience, and 
sustainability. By demonstrating that the concept is feasible in principle, QUANTAIR enables future SESAR projects 
to pursue targeted TRL 2 –3 developments with confidence. If successful, this pathway could, over the medium to 
long term, support Europe’s ambition to deliver the most environmentally friendly, efficient, resilient, and digitally 
integrated ATM system in the world. 
The achievement of these impacts depends on several factors beyond the scope of the project. Progress in quantum 
hardware and algorithms may be slower than anticipated, delaying the availability of scalable quantum methods. 
Conversely, rapid advances could create opportunities earlier than expected, underlining the importance of SESAR 
being prepared with conceptual frameworks and use cases that can be taken up as soon as technology allows. 
Stakeholders may also be cautious about adopting federated approaches if they perceive the governance, validation, 
or liability arrangements as unclear. Regulatory frameworks for higher airspace and environmental modelling may 
evolve at different speeds across regions, creating asymmetries in adoption. These are not risks to the project itself, 
but barriers that may influence whether the pathways identified can be realised in practice. By identifying them early, 
QUANTAIR ensures that future SESAR and policy work can anticipate these challenges and address them 
systematically. 
 
2.2 Measures to maximise impact - Dissemination, exploitation and communication #@COM-DIS-VIS-CDV@# 
The project adopts a proportionate but structured approach to dissemination, exploitation, and communication to 
ensure that its results are visible, usable, and positioned to generate impact beyond the immediate project lifetime. 
At TRL 1, the outputs are primarily conceptual frameworks, feasibility analyses, and structured assessments of 
Quantum Federated Learning (QFL) in ATM. The plan therefore emphasises knowledge sharing, policy relevance, 
and preparing pathways for follow-on research, rather than commercialisation. 
Communication activities will be modest but continuous, designed to ensure visibility of the project and to inform a 
wider audience about its purpose and benefits. The consortium will establish a project website as a primary 
information point, complemented by short news items and updates on professional social media platforms. These 
activities will highlight the relevance of quantum computing and federated learning to aviation in terms 
understandable to non-specialists, showing how such research contributes to efficiency, resilience, sustainability, and 
the safe integration of new entrants. Outreach will be proportionate to the scale of the project, relying on existing 
channels within the consortium, which collectively offers a global and cross-disciplinary network of contacts across 
research, industry, and regulatory communities. 
Dissemination will focus on the scientific and technical community, particularly those engaged in ATM 
modernisation and quantum computing research. Results will be published in open-access journals and presented at 
conferences such as SESAR Innovation Days  and relevant academic venues. Where appropriate, preprints will be 
released to encourage early engagement and peer feedback. The project will also contribute to SESAR knowledge-
sharing channels and technical workshops to ensure that results feed directly into the wider programme. 
Dissemination will be coordinated so that outputs are available in formats that are accessible and reusable, consistent 
with open science practices. 

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Exploitation will take place primarily through institutional and programme channels. The conceptual frameworks 
and feasibility assessments generated by QUANTAIR are intended for direct uptake by future SESAR research at 
TRL 2–3. They also provide material of interest to ATM stakeholders, who can use the findings to inform early 
thinking on data -sharing models and the potential of federated approaches. Regulators such as EASA, and 
international bodies such as ICAO, will also benefit from the insights generat ed, particularly in areas of higher 
airspace operations, contingency management, and environmental sustainability. Although no commercial 
exploitation is expected at TRL 1, the intellectual outputs represent a foundation that can be carried forward in 
subsequent projects or policy initiatives. Intellectual property will be managed in line with Horizon Europe rules, 
with ownership of results defined in the consortium agreement. Given the conceptual nature of the work, results will 
primarily be disseminated openly, with protection measures applied only if necessary to safeguard future exploitation 
potential. 
Feedback to policy measures is an integral part of the plan. The project’s outputs are directly relevant to ongoing 
European and international discussions, including EASA’s Higher Airspace Operations roadmap, ICAO’s ATMOPS 
and ATMRPP panels, and the global framework provided by the GATMOC. By sharing findings through technical 
workshops, regulatory consultations, and programme -level interactions, QUANTAIR will provide evidence to 
support policy development and help shape future research agendas. 
This first version of the dissemination and exploitation plan provides a framework that is appropriate to the scope 
and maturity of the project. A detailed plan will be produced as a mandatory deliverable within three months of 
project start and updated as  necessary during the project. Together, these measures will ensure that the outputs of 
QUANTAIR are widely disseminated, appropriately exploited, and communicated effectively to the scientific 
community, policymakers, stakeholders, and the public. 
 
References 
[1] Abbas, Amira, et al. "Challenges and opportunities in quantum optimization." Nature Reviews Physics (2024): 1-18. 
[2] Li, Li, et al. "A review of applications in federated learning." Computers & Industrial Engineering 149 (2020): 106854. 
[3] Ruiz, Sergio, et al. "Privacy-preserving federated machine learning in ATM: experimental results from two use cases." 
Proceedings of the 12th SESAR Innovation days (2022).  
[4] Ballester, Rocco, Jesus Cerquides, and Luis Artiles. "Quantum federated learning: a comprehensive literature review 
of foundations, challenges, and future directions." Quantum Machine Intelligence 7.2 (2025): 1-29. 
[5] SESAR Joint Undertaking, European ATM Master Plan 2025 Edition (2025). 
 
#§COM-DIS-VIS-CDV§# 

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2.3 Summary  
KEY ELEMENT OF THE IMPACT SECTION 
 
 
 
 
 
D & E & C MEASURES 
What dissemination, exploitation and communication 
measures will you apply to the results?  
 
Results will be communicated through a dedicated 
website, social media updates, and targeted news items 
for non-specialist audiences. Dissemination to the 
scientific community will use open-access journals, 
preprints, and conferences (SESAR Innovation Days, 
quantum/AI venues). Exploitation will occur through 
SESAR follow-on projects, input to regulatory 
roadmaps (EASA, ICAO), and alignment with 
international R&I activities. All outputs will be openly 
shared under FAIR principles, with repositories 
ensuring transparency and reuse. A mid-project and 
final dissemination event will engage stakeholders 
directly. 
EXPECTED RESULTS 
What do you expect to generate by the end of the 
project?  
 
By the end of the project, QUANTAIR will deliver: 
• Conceptual frameworks for applying QFL to 
four representative ATM challenges 
(departure predictability, higher -airspace 
integration, diversion capacity, contrail 
avoidance). 
• Feasibility analyses identifying assumptions, 
indicators, potential benefits, and limitations. 
• Roadmaps defining steps towards TRL 2 –3, 
including conditions under which quantum 
methods may provide added value. 
• Open, reproducible outputs (reports, models, 
synthetic datasets) supporting future SESAR 
research. 
 
SPECIFIC NEEDS 
What are the specific needs that triggered this 
project? 
 
Air Traffic Management (ATM) faces structural 
barriers to data sharing, scalability, and 
resilience. Operational datasets are fragmented 
across airlines, ANSPs, airports, and regulators, 
making centralised modelling infeasible due to 
sovereignty, confidentiality, and security 
constraints. At the same time, growing system 
complexity (multi-flight optimisation, 
integration of higher-airspace entrants, 
resilience to disruption, and contrail avoidance 
pushes classical methods to their limits. Europe 
requires a structured assessment of whether 
Quantum Federated Learning (QFL) can provide 
a privacy-preserving, scalable framework for 
collaborative modelling that is aligned with 
SESAR’s Digital European Sky ambitions. 
 
 

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#§IMP-ACT-IA§# 
TARGET GROUPS 
Who will use or further up-take the results of the 
project? Who will benefit from the results of the 
project?  
 
• SESAR research community and Joint 
Undertaking programme teams. 
• ATM operational stakeholders (airlines, 
ANSPs, airports, Network Manager). 
• Regulatory and policy bodies (EASA, ICAO 
panels, European Commission). 
• Academic and industrial researchers in 
federated learning, quantum computing, and 
AI for aviation. 
• Wider environmental and climate policy 
stakeholders interested in contrail mitigation 
and sustainable aviation. 
OUTCOMES 
What change do you expect to see after 
successful dissemination and 
exploitation of project results to the 
target group(s)? 
 
The project will provide the first 
structured evidence base for QFL in 
ATM, enabling SESAR to judge 
whether and how to pursue higher-
TRL development. Stakeholders will 
gain conceptual frameworks that 
inform data-sharing models, 
integration of new entrants, resilience 
strategies, and climate-optimised 
navigation. Dissemination ensures that 
results are embedded in SESAR 
roadmaps, regulatory debates, and 
international standardisation efforts. 
 
IMPACTS 
What are the expected wider scientific, economic and societal 
effects of the project contributing to the expected impacts outlined 
in the respective destination in the work programme? 
 
Wider impacts include: 
• Scientific: establishment of benchmarks and 
methodologies for QFL across distributed, safety -critical 
domains. 
• Economic: foundations for more efficient, resilient, and 
sustainable European ATM, reducing costs from delay, 
disruption, and inefficient fuel burn. 
• Societal: enhanced resilience of the air transport system, 
reduced climate impact via contrail avoidance, and 
strengthened European leadership in privacy-preserving AI 
and quantum research.  
In the longer term, QUANTAIR contributes directly to the 
Work Programme objective of delivering the most 
environmentally friendly, efficient, resilient, and digitally 
integrated ATM system worldwide. 

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3. Quality and efficiency of the implementation #@QUA-LIT-QL@# #@WRK-PLA-WP@#  
3.1 Work plan and resources  
The design of the QUANTAIR work plan reflects both the scope of a TRL 1 exploratory research project and the 
need to provide clear evidence of progress at defined review points. The structure is kept deliberately lean, with four 
work packages that separate management and outreach activities from the research, while ensuring that each of the 
project’s objectives is addressed in a coherent way. This approach avoids unnecessary fragmentation but still gives 
full visibility to the mandatory elements of management, dissemination, exploitation, and data management. 
 
 
GANTT-Chart overview including work packages, milestones and deliverables 
 
The first work package covers project management and the cross-cutting activities on communication, dissemination, 
and exploitation. This ensures that the day-to-day running of the project, financial and administrative tasks, reporting 
to the SESAR JU, and quality assurance are all handled in one place. The same package also includes the production 
and maintenance of the Project Management Plan, the Data Management Plan, and the Communication and 
Dissemination Plan. These are not treated as box -ticking exercises but as tools to give the consortium a common 
framework for how the work will be organised and how outputs will be shared with the wider research and 
stakeholder community. 
 
The research itself is divided into two phases. The first nine months (WP2) are dedicated to setting the foundations. 
This includes scoping the four exploratory cases - PREDICT, HORIQON, SHIELD, and CONTRADE - agreeing the 
assumptions and indicators that will be used to test feasibility, and producing the initial research plan and exploratory 
report. This early phase is designed to provide tangible outputs that can be reviewed and challenged at the interim 
stage, while still leaving scope for refinement. It  allows the consortium to put forward initial concepts and 
frameworks without claiming premature certainty, which is consistent with the TRL 1 nature of the project. 
 
The second research phase (WP3) runs from month 10 to month 18 and builds directly on the initial results. Here the 
consortium revisits the assumptions, tests them across the four cases, and develops the final research plan and report. 
The emphasis in this phase is on refinement and integration: drawing out the lessons that are common across cases, 
understanding where QFL may have genuine promise for ATM, and identifying the open questions that must be 
addressed in future TRL 2–3 research. This structure gives the project a rhythm: first to establish and scope, then to 


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refine and consolidate. By the end of month 18, the core research outputs are complete and ready for review. 
 
The final work package (WP4) is dedicated to ensuring that the results are not only written down but also 
communicated, preserved, and made available for exploitation. This includes the production of the final exploitation 
and impact strategy, compliance with open science and repository requirements, and the organisation of a final event 
to share the findings with both the research community and operational stakeholders. Dedicating a separate work 
package to this task provides clarity and ensures that disse mination and exploitation are not left to the margins of 
project management, but treated as substantive outputs in their own right. 
 
The sequencing of activities is straightforward. WP2 provides the initial research outputs and ensures that the 
consortium can demonstrate progress at the interim review. WP3 takes these results forward, deepening the analysis 
and completing the research e ffort. WP4 then consolidates the outputs and ensures their visibility. WP1 runs 
throughout, providing governance, coordination, and quality assurance. The interdependencies are limited but clear: 
WP2 feeds WP3, WP3 feeds WP4, and WP1 underpins the whole process. This simplicity is deliberate, allowing the 
consortium to concentrate on the substance of the research rather than the mechanics of coordination. 
 
Overall, the work plan balances clarity with proportionality. The structure makes it possible to demonstrate progress 
at the interim and final stages, while keeping the number of work packages aligned to the scale of a TRL 1 activity. 
Most of the effort is concentrated in the two research phases, where the exploratory cases are scoped, analysed, and 
refined. Management, communication, and exploitation activities run in parallel but remain light and focused, 
ensuring that resources are directed primarily to the research. In this way, the plan ensures that the project’s objectives 
can be met within the available timeframe and that the results are prepared for further development in future SESAR 
research. 
3.2 Capacity of participants and consortium as a whole #@CON-SOR-CS@# #@PRJ-MGT-PM@# 
The consortium for QUANTAIR is deliberately small and highly focused. It brings together three partners with 
complementary expertise that together cover the full range of knowledge needed for exploratory research on Quantum 
Federated Learning in Air Traffic Management. This structure avoids fragmentation while ensuring that the project 
objectives are addressed from scientific, operational, and regulatory perspectives. 
 
The disciplinary scope is broad. The project combines expertise in advanced computing and federated learning, in 
ATM modelling and SESAR research methods, and in the operational and regulatory environment of European 
aviation. This inter-disciplinary mix is central to the project’s feasibility. Quantum computing and federated learning 
alone cannot be meaningfully assessed without ATM expertise to define realistic use cases and indicators, and ATM 
modelling by itself cannot explore the potential of novel com putational paradigms. By combining the three, the 
project ensures that technical exploration remains grounded in operational reality, while operational questions are 
informed by the latest research in computing and data science. 
 
Each partner contributes distinct capabilities. One partner provides established experience in ATM research and 
modelling, including the frameworks and methods that underpin SESAR exploratory research. A second partner 
contributes deep expertise in quantum computing and federated learning, with access to state-of-the-art algorithmic 
research and computational frameworks. The third partner contributes operational and regulatory insight, drawing on 
direct experience with ATM stakeholders, industry bodies, and  international regulators. This combination ensures 
that the project not only develops and analyses conceptual frameworks, but also tests their plausibility in the context 
of the real ATM system. 
 
The consortium also has access to the infrastructures required for this work. The ATM research partner maintains 
modelling platforms and reference datasets that support exploratory analysis in a SESAR context. The quantum 
computing partner has access to qu antum simulators and federated learning frameworks suitable for conceptual 
testing. The operational partner brings established stakeholder networks and access to policy and regulatory processes 
that allow the project results to be evaluated in relation to real-world governance and adoption constraints. Together, 
these infrastructures provide the project with all the tools needed for TRL 1 exploration. 

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The partners complement one another across the research chain. The computing expertise ensures that new methods 
are explored rigorously, the ATM research expertise ensures that methods are tested against relevant operational 
questions, and the operational expertise ensures that outputs are credible for stakeholders and aligned with regulatory 
processes. None of these alone would be sufficient; together they provide a coherent and proportionate team for the 
project. 
 
Cross-cutting considerations are also addressed. The consortium applies open science practices in line with Horizon 
Europe requirements, including publication in open -access journals, sharing of analytical frameworks, and 
compliance with FAIR data management principles. The project also acknowledges that the integration of the gender 
dimension in R&I is not directly relevant to the scientific content, since the research addresses computational and 
operational questions without differentiated gender impacts. However, dissemination and communication activities 
will be inclusive, with attention to diversity in language, imagery, and stakeholder engagement. 
 
Overall, the consortium is proportionate to the scale and ambition of QUANTAIR. It combines complementary 
expertise in computing, ATM research, and operational regulation, has access to the required infrastructures, and is 
well positioned to deliver the pr oject’s objectives while preparing the ground for future SESAR research at higher 
maturity levels. 
 
#§CON-SOR-CS§# #§PRJ-MGT-PM§# 
Tables for section 3.1 
Table 3.1a:  List of work packages 
Work 
package 
No 
Work Package 
Title 
Lead 
Participant 
No 
Lead 
Participant 
Short Name 
Person-
Months 
Start 
Month 
End 
month 
1 Project 
Management & 
CDE 
1 DLR 10 01 24 
2 QUANTAIR 
Research 
Activity, Part A 
2 Qoro 18 01 09 
3 QUANTAIR 
Research 
Activity, Part B 
2 Qoro 18 09 18 
4 CDE Wrap-up 3 SkyNav 4 18 24 
 
 
Table 3.1b: Work package description  
For each work package:  
Work package number  1 
Work package title Project Management & CDE 
 

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Objectives  
This work package ensures that the project is managed efficiently and in full alignment with Horizon Europe 
and SESAR 3 JU requirements. It provides governance, financial and administrative management, reporting, 
and quality assurance across the project. I t also covers the planning and execution of communication, 
dissemination, exploitation, and data management activities, ensuring that results are properly documented and 
shared. In doing so, it establishes the framework for the project’s visibility and impact, and leads directly into 
the final CDE phase (WP4) where these activities are consolidated and completed. 
The specific objectives are to: 
1. Ensure effective project governance and coordination, including quality assurance and compliance 
with SESAR 3 JU requirements. 
2. Establish and maintain the project’s management framework through delivery and updating of the 
Project Management Plan (PMP), Data Management Plan (DMP), and related documentation. 
3. Provide timely and accurate reporting, including periodic reports and preparation for SESAR reviews. 
4. Plan and implement communication, dissemination, and exploitation activities, including the 
Communication and Dissemination Plan (CDE), project website, online presence, and events. 
5. Support long-term uptake and impact by drafting and finalising the exploitation and impact strategy, 
ensuring compliance with open science and FAIR data requirements, and preparing the transition into 
the final CDE wrap-up phase. 
 
Description of work  
Governance will be provided by a Project Management Board (PMB), chaired by the Coordinator and 
composed of one representative from each partner. The PMB will meet quarterly to review progress, monitor 
risks, and take decisions on both technical and admini strative issues. The Project Coordinator will manage 
day-to-day operations and act as the primary interface with SESAR 3 JU. A Kick-off Meeting (KoM) in Month 
1 will formally establish working procedures, confirm roles and responsibilities, and launch project activities. 
 
A Project Management Plan (PMP) will be delivered in Month 3 to formalise procedures for governance, 
reporting, quality assurance, and risk management. This will include internal review mechanisms to ensure 
that all deliverables are technically sound, consistent, and aligned with SESAR standards before submission. 
The PMP will be updated at mid -point (M12) to reflect lessons learned and adjustments. Progress will be 
monitored continuously, with corrective measures applied promptly in case of deviations. 
 
Research data will be managed under a Data Management Plan (DMP), produced in Month 3 and updated as 
needed. The DMP will describe the types of data generated, their provenance, and how FAIR principles 
(Findable, Accessible, Interoperable, Reusable) will be applied. Where confidentiality or security constraints 
apply, these will be documented with clear justification. Datasets and analytical outputs will be preserved in 
trusted repositories with persistent identifiers to ensure long-term access. 
 
The Communication, Dissemination and Exploitation (CDE) Plan will also be delivered in Month 3 and 
updated at least once during the project (M12). It will define the channels and activities to ensure visibility and 
uptake of results, including the project website, open -access publications, contributions to SESAR 
dissemination events, and targeted outreach to scientific, operational, and policy audiences. A mid -project 
dissemination event will be held at M12 to share initial results and prepare the ground for the final wrap -up 
phase. WP1 therefore establishes the framework for CDE activities, while the final consolidation and impact 
reporting are delivered in WP4. 
 
Exploitation measures will focus on enabling results to be taken forward in future SESAR research and by 
stakeholders such as ANSPs, regulators, and ICAO working groups. Intellectual property will be managed in 

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line with Horizon Europe rules and the consortium agreement, with a principle of openness unless specific 
protection is required to preserve exploitation potential. A draft post-research exploitation and impact strategy 
will be delivered in M24, which then feeds into WP4 for finalisation and promulgation. 
 
Reporting will cover all mandatory submissions to SESAR 3 JU, including periodic reports at the end of each 
reporting period and the final project report at closure. SESAR review meetings will be treated as milestones 
in project governance. The consortium will prepare collectively for these reviews, ensuring that deliverables, 
presentations, and supporting evidence are complete and coherent. Internal review meetings will be scheduled 
in advance to prepare for each review and ensure alignment with SESAR expectations. 
 
 
 
Work package number  2 
Work package title QUANTAIR Research Activity, Part A 
 
Objectives  
This work package establishes the conceptual and analytical foundations for applying Quantum Federated 
Learning (QFL) in Air Traffic Management. It provides the first structured exploration of how QFL could be 
framed within ATM constraints, how it might operate across distributed stakeholders, and what benefits or 
limitations may arise in different operational contexts. The emphasis is on scoping and structuring the research, 
and on producing verifiable outputs that are consistent with the TRL 1 nature of the project and suitable for 
review at the end of the first reporting period. 
The specific objectives are to: 
1. Define the conceptual framework for QFL in ATM, including assumptions on data distribution, 
privacy constraints, stakeholder behaviour, and computational resources. 
2. Scope and characterise the four exploratory cases (PREDICT, HORIQON, SHIELD, CONTRADE), 
identifying the ATM challenges they represent and their relevance to SESAR priorities. 
3. Develop baseline analytical models for each case, using synthetic data and structured representations 
of ATM processes to test conceptual feasibility. 
4. Identify potential benefits, limitations, and open research questions arising from each case, recording 
these in interim deliverables. 
5. Produce a consolidated interim report that synthesises findings across all cases and highlights cross -
cutting requirements for advancing to TRL 2–3 research. 
 
 
Description of work  
The purpose of WP2 is to establish the conceptual and analytical basis of the project. The first nine months are 
dedicated to scoping the research in a structured way, producing tangible outputs that allow progress to be 
verified at the interim review. This reflects both the TRL 1 nature of QUANTAIR and the SESAR requirement 
for clear evidence of achievement within the first reporting period. The work ensures that Quantum Federated 
Learning is not treated as an abstract concept, but is defined in relation to the constraints and opportunities of 
Air Traffic Management. 
 
The work begins with the development of the conceptual framework for QFL in ATM. This framework sets 
out the core assumptions on which the research is based, including the distribution and heterogeneity of data 
sources, the privacy constraints that prevent  raw data exchange, the behavioural preferences of stakeholders 

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such as airlines’ cost -index settings, and the computational resources that can realistically be assumed at 
different maturity levels. It also addresses governance considerations, such as how trust and participation are 
established between stakeholders, and what indicators of feasibility can be applied at TRL 1. This step creates 
the common reference point against which each exploratory case can be analysed. 
 
Each of the four exploratory cases is then scoped in detail. For PREDICT, HORIQON, SHIELD, and 
CONTRADE, the partners will describe the operational challenge, its connection to SESAR Master Plan 
objectives, and the specific way in which QFL could apply. This scoping ensures that the project covers a 
broad spectrum of ATM challenges (predictability, high -altitude integration, resilience, and environmental 
optimisation) rather than variations on a single theme. It also defines for each case the boundaries of what can 
be assessed at TRL 1, the types of data or models that would be relevant, and the expected indicators of benefit 
or limitation. 
 
Once the scoping is complete, baseline analytical models will be developed for each case. These models will 
use synthetic data and structured representations of ATM processes, rather than operational datasets, to ensure 
that the work remains feasible within a TRL 1 scope. The models are intended to test conceptual feasibility: 
whether QFL can in principle represent the ATM dynamics involved, whether stakeholder constraints can be 
incorporated in a distributed way, and whether potential benefits can be expressed in measurable terms. This 
activity requires close collaboration between the partners: the quantum computing expertise to design federated 
and distributed model structures, the ATM research expertise to represent operational processes, and the 
operational expertise to validate the plausibility of assumptions. 
 
Findings from the framework and case models will be captured continuously and compared across cases. This 
cross-case analysis is a critical part of WP2: it ensures that common themes are identified, that lessons from 
one case inform the others, and that ga ps and limitations are recorded in a way that can guide WP3. Interim 
deliverables, including the Exploratory Research Plan (ERP), Exploratory Research Report (ERR), and 
Concept Outlines, provide formal records of this work. These deliverables will undergo internal review and 
PMB approval before submission to SESAR 3 JU, ensuring technical soundness and alignment with SESAR 
standards. 
 
Resource allocation in WP2 reflects the intensity of this phase. All three partners are engaged in parallel across 
the four cases, with effort distributed according to expertise: ATM research leads the case scoping and 
conceptual assumptions, the quantum partner leads federated modelling structures, and the operational partner 
ensures realism, regulatory alignment, and tra ceability to stakeholder concerns. The effort in WP2 is 
proportionately the largest, as it defines the trajectory for the remainder of the project and provides the evidence 
required at the first review milestone. 
 
By the end of WP2, the project will have established a conceptual framework for QFL in ATM, scoped and 
modelled its application across four distinct operational contexts, and produced the first structured assessment 
of feasibility. These outputs provide the foundation for refinement and integration in WP3, and allow SESAR 
to judge progress at the interim stage with confidence that the project is on track. 
 
 
Work package number  3 
Work package title QUANTAIR Research Activity, Part B 
 
Objectives  
This work package builds directly on the foundations established in WP2. Whereas WP2 defined the 

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conceptual framework and developed initial models for the exploratory cases, WP3 is focused on refinement, 
cross-case synthesis, and the production of final research outputs. The emphasis is on consolidating results 
into a coherent assessment of Quantum Federated Learning (QFL) in ATM, and on defining realistic pathways 
for future research at higher maturity levels. WP3 therefore completes the scientific work of the project before 
the CDE wrap-up phase begins. 
The specific objectives are to: 
1. Refine and extend the exploratory case models developed in WP2, incorporating lessons learned and 
addressing the open questions identified in the first reporting period. 
2. Conduct systematic cross-case analysis to identify common challenges, benefits, and limitations of 
QFL across diverse ATM domains. 
3. Develop structured recommendations for advancing QFL research towards TRL 2 –3, including the 
conditions under which quantum machine learning and other quantum methods could provide 
additional value. 
4. Produce a consolidated final research report that synthesises the project’s findings, documents 
conceptual advances, and defines priorities for future SESAR research. 
5. Frame the results to support dissemination and exploitation, ensuring that outputs are relevant for 
SESAR follow-on projects, ATM stakeholders, and international bodies such as ICAO and EASA. 
 
 
Description of work  
WP3 represents the second phase of the research activity and builds directly on the foundations established in 
WP2. While the first nine months of the project are focused on scoping and baseline modelling, the following 
nine months are dedicated to refinement, integration, and the delivery of the final research outputs. The aim is 
to consolidate the work of the exploratory cases into a coherent assessment of Quantum Federated Learning in 
ATM, and to provide recommendations and pathways for how this early-stage research can be taken forward 
at higher maturity levels. 
 
The work begins with refinement of the exploratory case models. Each case - PREDICT, HORIQON, SHIELD, 
and CONTRADE - will be revisited using the frameworks and assumptions established in WP2. The baseline 
models developed earlier will be extended to incorporate lessons learned, address gaps identified in the interim 
review, and respond to stakeholder feedback where appropriate. Refinement may include adjusting 
assumptions on data distribution or stakeholder behaviour, testing alternative formulations of the  federated 
learning approach, or clarifying the conditions under which conceptual feasibility is most evident. This ensures 
that the case outputs are not static one -off analyses but part of an iterative process of validation and 
improvement. 
 
Once the case models have been refined individually, the consortium will conduct systematic cross -case 
analysis. The purpose of this activity is to extract common themes and divergences across the four contexts. 
For example, it may be that QFL appears most promising in situations where privacy constraints dominate (as 
in SHIELD or CONTRADE), but less so where operational predictability is already supported by centralised 
models (as in PREDICT). Cross -case analysis allows the consortium to identify these patterns, quantify 
similarities and differences where possible, and derive generalisable insights. It also ensures that the project 
delivers more than four separate case studies: the results are integrated into a broader understanding of where 
QFL can contribute to ATM and where its limitations are most apparent. 
 
In parallel, WP3 develops structured recommendations for the future. These recommendations will set out the 
steps required to advance QFL research to TRL 2 –3, including what kinds of datasets, computational tools, 
and stakeholder collaborations will be needed. They will also identify where quantum optimisation and other 
quantum computing methods could in future provide additional value, recognising that current hardware is not 

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Part B - Page 27 of 50 
 
yet sufficient for operational validation but that pathways towards higher maturity can be mapped. The 
recommendations will be positioned within the SESAR programme context, linking to Master Plan objectives 
and ensuring coherence with European and international research agendas. 
 
The culmination of WP3 is the production of the final consolidated research report. This document will 
synthesise the project’s findings, document the conceptual advances achieved, and set out the open questions 
that remain. It will provide the first structured TRL 1 assessment of QFL in ATM, written in a way that supports 
both technical follow -on research and policy -level consideration. The report will be accompanied by 
consolidated concept outlines for each case, ensuring that results are clearly traceabl e and can be compared 
across contexts. 
 
The work in WP3 also contributes directly to dissemination and exploitation. By framing results in a way that 
is relevant for SESAR follow -on projects, ATM stakeholders, and international bodies such as ICAO and 
EASA, the outputs of WP3 are designed to be immediately useful beyond the project itself. This includes 
highlighting potential contributions to international guidance documents (such as ICAO’s GATMOC) and to 
regulatory roadmaps such as the EASA AI and Higher Airspace Operations strategies. 
 
The resources allocated to WP3 reflect the need for intensive collaborative work. All partners are fully 
engaged: quantum computing expertise is used to refine federated modelling structures and assess long -term 
scalability; ATM research expertise leads th e cross -case synthesis and ensures coherence with SESAR 
frameworks; and operational and regulatory expertise ensures that recommendations are framed in a way that 
is credible to stakeholders and aligned with the wider aviation ecosystem. WP3 therefore represents the point 
where the strands of the project are brought together, refined, and translated into outputs that are not only 
scientifically sound but also operationally meaningful. 
 
By the end of WP3, the project will have moved from initial feasibility exploration to a consolidated set of 
findings. The results will provide a clear picture of how QFL could contribute to ATM, what limitations must 
be addressed, and what steps are required to move forward. This provides a natural transition into WP4, where 
dissemination, exploitation, and open science compliance are finalised and the project’s impact is secured. 
 
Work package number  4 
Work package title Post Research CDE Activity 
 
Objectives  
This work package consolidates and amplifies the results of the project, ensuring that the outputs of 
QUANTAIR are visible, accessible, and usable for future research, regulatory processes, and operational 
stakeholders. While CDE activities are initiated in WP1, the final phase provides dedicated focus o n 
exploitation, open science compliance, and high -impact dissemination. WP4 demonstrates the consortium’s 
commitment to maximising the value of Horizon Europe and SESAR investment, by delivering results that are 
technically credible, operationally relevant, and aligned with European and international aviation strategies. 
The specific objectives are to: 
1. Finalise the exploitation and impact strategy, translating project results into clear recommendations 
for SESAR follow -on activities, national and European research agendas, and international bodies 
such as ICAO and EASA. 
2. Ensure compliance with open science and FAIR principles , producing a repository of project 
outputs (reports, models, datasets, metadata) in trusted long-term archives with persistent identifiers. 
3. Maximise dissemination to the research community , through open -access publications, 
contributions to SESAR dissemination events, and active presence in scientific fora. 

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4. Promote visibility towards operational and policy stakeholders , including ANSPs, airlines, 
regulators, and industry, by framing project results in terms of operational feasibility, policy relevance, 
and long-term research pathways. 
5. Deliver a final public event and supporting materials, showcasing the project’s achievements and 
providing a platform for dialogue with the wider ATM community, demonstrating the value of SESAR 
exploratory research and its contribution to the Digital European Sky vision. 
 
Description of work  
WP4 consolidates the results of the project and ensures that they are visible, accessible, and exploitable beyond 
the lifetime of QUANTAIR. While communication, dissemination, and exploitation activities are initiated 
under WP1 to give early visibility, th is final phase provides dedicated focus on maximising impact. The 
emphasis is on producing a final exploitation and impact strategy, ensuring compliance with open science 
principles, and delivering a high-quality public event that promotes the project’s results to both technical and 
policy audiences. 
 
The first strand of work concerns the exploitation and impact strategy. A draft version is developed under 
WP1, but in WP4 this is finalised based on the complete research results. The strategy translates conceptual 
findings into concrete recommendations for SESAR follow-on activities, as well as guidance for national and 
European research agendas. It also identifies how the results can support international bodies such as ICAO, 
for example through inputs to the Global Air Traffic Management Operational Con cept (GATMOC), and 
EASA, through alignment with the AI Roadmap and Higher Airspace Operations strategy. By framing the 
results in terms of both research and policy pathways, the strategy ensures that QUANTAIR provides 
maximum value to SESAR and the wider aviation community. 
 
The second strand addresses open science and data preservation. A final repository of project outputs will be 
prepared, ensuring that reports, conceptual models, metadata, and other analytical products are made available 
in trusted long-term archives. All materials will carry persistent identifiers to guarantee findability and access, 
and licensing conditions will be clearly stated to encourage reuse where possible. This ensures that the research 
remains transparent and accessible, in line with the FAIR pri nciples (Findable, Accessible, Interoperable, 
Reusable) and Horizon Europe’s open science obligations. Sensitive or restricted material will be clearly 
documented, with justifications for any limitations on access. 
 
The third strand is dissemination to the research community. Results will be submitted for publication in open-
access journals and conference proceedings, ensuring peer-reviewed visibility and providing a foundation for 
subsequent academic and industrial r esearch. The consortium will also contribute to SESAR dissemination 
events, workshops, and thematic fora, presenting QUANTAIR as a case study of how Horizon Europe 
exploratory research can be used to investigate emerging concepts at TRL 1. This creates continuity with other 
SESAR activities and supports the integration of results into the wider Digital European Sky programme. 
 
A fourth strand focuses on visibility towards operational and policy stakeholders. While the research is 
conceptual, its framing will be tailored to the concerns of ANSPs, airlines, regulators, and manufacturers. 
Materials will highlight the operational implications of the findings, such as the handling of sensitive airline 
preferences (e.g. cost index), or the potential for privacy-preserving collaboration on contrail avoidance. The 
project will also prepare concise policy briefs, targeted at European institutions, to make the research outputs 
accessible to decision-makers without requiring them to engage with technical detail. 
 
Finally, WP4 delivers a high-impact final dissemination event. This event, organised at the end of the project, 
will bring together the SESAR research community, operational stakeholders, and policy representatives. It 
will showcase the project’s achievements, present the consolidated results, and provide a forum for discussion 

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Part B - Page 29 of 50 
 
on how QFL research should be taken forward. Supporting materials, including an event report, presentations, 
and a digital archive of outputs, will ensure that the visibility of the project extends beyond the event itself. 
The work in WP4 requires contributions from all partners. The ATM research partner ensures that the 
exploitation strategy is aligned with SESAR frameworks. The quantum computing partner provides visibility 
on how the conceptual work can evolve as technolog y matures. The operational partner ensures that 
dissemination is framed in terms that resonate with industry and regulators. Together, the consortium ensures 
that the final outputs are technically credible, operationally meaningful, and presented in a way that maximises 
their uptake. 
 
By the end of WP4, QUANTAIR will have delivered not only a comprehensive set of scientific results, but 
also the supporting materials, strategies, and outreach activities needed to ensure that those results have impact. 
The project will leave behind a transparent and accessible record of its findings, contribute to SESAR’s long-
term research programme, and demonstrate the role of Horizon Europe exploratory research in shaping the 
future of ATM. 
 
Table 3.1c: List of Deliverables   
Nr. Deliverable 
name Short description 
Work 
package 
number 
Short 
name of 
lead 
participant 
Type 
Disse
minati
on 
level 
Delivery 
date 
(in 
months) 
1.1 KOM Report Report from the Kick-off 
Meeting 1 DLR R PU 1 
1.2 Initial PMP 
Initial Project 
Management Plan 1 DLR R PU 3 
1.3 Initial CDE 
Plan 
Initial Communication, 
Dissemination & 
Exploitation plan 
1 SkyNav R PU 3 
1.4 Online 
presence 
Project Website and 
Online presence 1 SkyNav DEC PU 3 
1.5 Initial DMP Initial Data Management 
Plan 1 DLR R PU 6 
1.6 HE Report Periodic HE technical and 
Financial report 1 Qoro R CO 9 
1.7 Updated 
PMP 
Updated Project 
Management Plan 1 DLR R PU 12 
1.8 Updated 
CDE Plan 
Updated Communication, 
Dissemination & 
Exploitation plan 
1 SkyNav R PU 12 
1.9 Mid-project 
Event 
Information event about 
research progress 1 SkyNav OTH PU 12 
1.10 Updates 
DMP 
Updated Data 
Management Plan 1 DLR R PU 15 
1.11 Final project 
report Final project report 1 DLR R CO 24 

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2.1 Initial ERP Initial Exploratory 
Research Plan 2 Qoro R PU 4 
2.2 Initial ERR Initial Exploratory 
Research Report 2 Qoro R PU 6 
2.3 Initial CO Initial Concept Outline 2 Qoro R PU 6 
3.1 Final ERP Final Exploratory Research 
Plan 3 Qoro R PU 16 
3.2 Final ERR Final Exploratory Research 
Report 3 Qoro R PU 18 
3.3 Final CO Final Concept Outline 3 Qoro R PU 18 
4.1 Initial EI 
Strategy 
Initial Post-Research 
Exploitation & Impact 
Strategy 
4 SkyNav R PU 20 
4.2 Final EI 
Strategy 
Final Exploitation & Impact 
Strategy 4 SkyNav R PU 24 
4.3 Compliance 
Report 
Repository & Open Science 
Compliance Report 4 SkyNav R PU 24 
4.4 Final Event 
Report 
Final Promulgation Event 
Report 4 SkyNav R PU 24 
 
Table 3.1d: List of milestones  
Milestone 
number 
Milestone name Related work 
package(s) 
Due date (in month) Means of verification 
MS1.1 Project Governance 
Established 
1 1 PMB in place; KoM held; 
PMP development 
launched. 
 KoM report (D1.1). 
MS1.2 
 
Management and 
CDE Frameworks 
Approved 
1 3 PMP, DMP, and initial 
CDE Plan delivered and 
accepted. 
Submission of 
D1.2/1.3/D1.5. 
MS1.3 
 
Mid-Project Review 
Prepared  
1 12 Updated PMP and CDE 
delivered; Mid-project 
event held. 
Submission of D1.5–D1.8. 
MS1.4 
 
Draft Exploitation & 
Impact Strategy 
Produced 
 
1 18 Exploitation and impact 
strategy delivered in draft, 
ready for hand-over to 
WP4. 
Submission of D1.10. 
MS1.5 
 
Final Review & 
Project Closure 
1, 4 24 Final project report 
delivered; SESAR final 
review completed. 
Acceptance of D1.9–
D1.10. 
 

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Table 3.1e: Critical risks for implementation #@RSK-MGT-RM@# 
Description of risk (indicate level of 
(i) likelihood, and (ii) severity: 
Low/Medium/High) 
Work package(s) 
involved 
Proposed risk-mitigation measures 
R1. QFL concept does not yield 
meaningful insights at TRL 1 
(Likelihood: Medium; Severity: High). 
Interim models show no plausible 
feasibility signals across exploratory 
cases. 
WP2 WP3 Treat inconclusive or negative results as 
valid TRL 1 outcomes; ensure each case 
has at least two formulations so 
alternatives can be tested; document 
limits clearly and extract learning for 
future TRL 2–3 work. 
R2. Assumptions and frameworks 
diverge between cases leading to 
inconsistent or incomparable results 
(Likelihood: Medium; Severity: 
Medium). 
WP2 WP3 
 
Maintain a common framework agreed 
in D2.1; record all assumptions in an 
“assumptions register” reviewed by 
PMB; hold cross-partner review sessions 
before submitting deliverables. 
R3. Baseline models cannot capture 
distributed ATM constraints 
realistically (privacy, governance, 
airline cost-index preferences) 
(Likelihood: Medium; Severity: 
Medium). 
WP2 Start with simplified synthetic scenarios; 
expand gradually; involve operational 
experts early; record limitations 
explicitly in interim reports. 
R4. Exploratory cases overlap too 
much, reducing distinct insights 
(Likelihood: Medium; Severity: 
Medium). 
WP2 WP3 
 
Enforce differentiation when scoping 
cases; PMB validates distinct objectives 
for each; adjust case emphasis if overlap 
is detected. 
R5. Over-reliance on synthetic data 
reduces credibility of results 
(Likelihood: Medium; Severity: 
Medium). 
WP2 WP3 
 
Anchor synthetic datasets to published 
SESAR references; use sensitivity 
analyses to show robustness; invite 
stakeholder feedback at mid-project 
event. 
R6. Role of quantum computing is 
overstated or too vague (Likelihood: 
Medium; Severity: High). 
WP2 WP3 
 
Link all quantum-related statements to 
clear conditions of validity; maintain a 
“claims register”; ensure internal peer 
review of evidence before submission. 
R7. Limited stakeholder engagement 
reduces visibility and uptake 
(Likelihood: Low; Severity: Medium). 
WP1 WP4 
 
Use consortium networks across ATM, 
regulatory, and research communities; 
organise mid-project and final events; 
publish open-access outputs and policy 
briefs. 
R8. Unavailability of key staff due to 
illness, turnover, or conflicting 
commitments (Likelihood: Medium; 
Severity: Medium). 
WP1 WP2 WP3 WP4 
 
Redistribute effort across the consortium 
where possible, using flexibility in the 
Horizon Europe rules; adjust timelines 
for lower-priority tasks to protect critical 
deliverables; replace staff within the 
same partner if necessary, ensuring 
continuity of expertise. 
 
#§RSK-MGT-RM§# 
 

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Part B - Page 32 of 50 
 
Table 3.1f:  Summary of staff effort 
 WP1 WP2 WP3 WP4 Total Person- 
Months per Participant 
1/DLR  5 5 5 1 16 
2/Qoro  6 14 12 3 35 
3/SkyNav  11.8 6 6 4 27.8 
Total Person Months 22.8 25 23 8 78.8 
 
Table 3.1h: ‘Purchase costs’ items (travel and subsistence, equipment and other goods, works and 
services)  
1/DLR 
 Cost (€) Justification 
Travel and subsistence  5000 Travel for conferences, workshops, and team meetings 
 
Equipment  -  
Other goods, works and 
services 
-  
Remaining purchase costs 
(<15% of pers. Costs) 
-  
Total 5000  
 
2/Qoro 
 Cost (€) Justification 
Travel and subsistence  12500 Travel for conferences, workshops, and team meetings 
 
Equipment  -  
Other goods, works and 
services 
2000 Consortia events, venue and facilities 
Remaining purchase costs 
(<15% of pers. Costs) 
-  
Total 12500  
 
3/SkyNav 
 Cost (€) Justification 
Travel and subsistence  17500 Travel for conferences, workshops, and team meetings 
 
Equipment  -  
Other goods, works and 
services 
-  
Remaining purchase costs 
(<15% of pers. Costs) 
-  
Total 17500  
 
#§QUA-LIT-QL§# #§WRK-PLA-WP§# 

=== PAGE 66 ===
 
 
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