Shannon Whitlock

2022–2027 €5M total €1.14M Unistra PI

EuRyQa is an EU-funded initiative advancing ultracold atom quantum computing as a leading platform for scalable digital quantum technologies. The project brings together eleven partners from seven countries — including academic leaders and industry — to develop fully programmable and scalable quantum processors based on Rydberg atoms.

Key features and goals

• Unites four complementary European Rydberg quantum computing platforms for cross-validation and rapid scaling
• Establishes the first common benchmarks and standards for neutral-atom quantum processors in Europe
• Combines academic expertise with industry partners in quantum hardware, electronics, and software
• Provides a shared quantum computing stack and cloud service to the European research and innovation community

EuRyQa has received funding under Horizon Europe programme HORIZON-CL4-2021-DIGITAL-EMERGING-01-30 via project 101070144.

euryqa.eu →

2024–2028 €2.53M (EU) Coordinator

MLQ is a coordinated doctoral training network to explore how ideas and techniques from machine learning and quantum can feed into and benefit the other. We provide high-level interdisciplinary training to 10 doctoral researchers who will become leaders of this new frontier.

The doctoral researchers explore how machine learning and quantum science technology can be combined to (i) extend quantum and classical machine-learning prediction of materials and matter properties to strongly-correlated regimes, and (ii) accelerate the development of quantum technologies through machine learning — opening new approaches to problems currently out of reach for classical computers.

Funded by the European Union’s Horizon Europe research and innovation programme under Marie Skłodowska-Curie grant agreement number 101120240.

mlq.quantumexcellence.org →

2025–2027 €1M total €302k Unistra PI

We propose an approach to enhance qubit coherence by orders of magnitude, by storing quantum information in the lowest energy states of small qubit clusters. This encoding is protected from major sources of decoherence due to a high degree of spatial symmetry supported by long-range interactions. We apply these principles to both Rydberg atoms and superconducting circuits — architectures with the required properties — and develop protocols to couple, control, read out, and benchmark the qubits.

Consortium

• Coordinator: Eran Ginossar (University of Surrey, UK)
• Shannon Whitlock (University of Strasbourg, FR)
• Marzena Szymanska (University College London, UK)
• Michael Stern (Technion, IL)

Funded within the QuantERA II Programme, which has received funding from the EU H2020 research and innovation programme under GA No 101017733.

MIMIQ is a quantum circuit simulation framework built at QPerfect, with Python and Julia front-ends and a cloud backend for large-scale jobs. It pairs an exact state-vector simulator with a matrix-product-state engine that compresses weakly-entangled states, so the same circuit can be probed on small systems exactly and pushed to thousands of qubits with controlled approximation.

Capabilities

• Two backends. State-vector (exact, up to ~32 qubits) and matrix-product states (large-scale, exact when entanglement is bound or approximate with a reported fidelity).
• Full operation library. Unitary gates, non-unitary projectors and conditionals, noise via Kraus operators, statistical ops (expectation values, entanglement measures), and composite primitives (QFT, oracles, custom gates).
• Interoperable. Imports and runs circuits from OpenQASM and Stim; native Protobuf format for round-tripping MIMIQ programs.
• Remote execution. Submit jobs to the QPerfect cloud for HPC-scale simulations directly from a notebook.

Benchmarks: comparative benchmarking paper · independent results from Quantinuum.

QPerfect → Documentation Launch talk (Q2B Tokyo 2024)

VeraCite checks the accuracy and conformity of BibTeX/biblatex bibliographies in scientific articles before publication. It surfaces incorrect DOIs, malformed entries, mismatched metadata, and hallucinated references — the kinds of errors that creep in when citations are generated or edited by large language models — without ever modifying your source files.

How it works

• Syntax validation. BibTeX/biblatex conformance, duplicate keys and DOIs, title casing, and invalid identifiers (DOI check digits, arXiv format, ISBN, ISSN, ORCID).
• Semantic checks. Author, title, year, and venue cross-checked against online registries — Crossref, arXiv, INSPIRE-HEP, OpenAlex, and Open Library — flagging preprints with published versions, retractions, and errata.
• Context analysis. Optional citation-relevance checks against the manuscript itself.

Every check derives from an explicit rule or a registry comparison — deterministic and auditable, never a guess. It reports an integrity and a confidence score, in human-readable or machine-readable NDJSON output, and stays local unless context analysis is explicitly opted in.

Written in Python, MIT-licensed. pip install veracite then veracite --bib refs.bib.

GitHub → Try the web demo

QG8 is a compact, extensible specification for serialising quantum circuits, states, and tensor objects in a way that is portable between simulators, hardware backends, and analysis pipelines.

github.com/aQCess/QG8 →

The principles of quantum mechanics — which govern physics, chemistry, material science, and computing at the nanoscale — are leading a wave of new technologies that have the potential to revolutionise how we communicate, process information, and learn about our world. This course aims to give you the tools to understand, develop, and apply quantum technologies, so you can be part of the second quantum revolution. While primarily targeting physics students, it is designed to be accessible to engineering, mathematics, and computer science students seeking a deeper understanding of underlying principles.

Course structure

• Part A — Concepts and Foundations. Core principles of quantum mechanics and light–matter interaction.
• Part B — Physical Platforms. Superconducting qubits, atomic quantum systems, and photonic quantum technologies.
• Part C — Applications. Quantum computing, sensing, and communication (in development).

Read Chapters 1 & 2 (PDF) →

Teachers interested in offering this course to their students can get in touch.

2026–2029 €19.8M total 70+ partners Hub coordinator & WP leader

EQA is preparing the workforce Europe will need to lead in a quantum sector expected to surpass €155 billion globally by 2040. The initiative brings together more than 70 partner institutions and over 100 affiliated organisations across Europe to coordinate quantum education across the full learning pipeline — from schools and universities through professional upskilling and lifelong learning.

Key goals

• Coordinate quantum education across the full learning pipeline.
• Connect talent, education, and industry through six Regional Quantum Academies spanning Northern, Western, Southern, Eastern, and Iberian Europe.
• Help reduce gaps in access to quantum education and opportunities across the continent.

My role

Co-leading a Regional Quantum Academy with partners across the Upper-Rhine and Alpes regions, and co-leading the Work Package on doctoral training.

As Jacob Sherson, EQA Coordinator, puts it: “Europe’s quantum sovereignty will not be secured in a handful of elite labs. It will be secured by connecting the full pipeline — from the classroom to the cleanroom.”

EFEQT — “Empowering the Future Experts in Quantum Science and Technology for Europe” — is a one-year complementary training for students enrolled in a European MSc programme, recognised by a European Master certificate backed by 12 European research institutions. Each year we host around 50 students from dozens of countries who benefit from networking opportunities, mentoring, regular meetups with experts from academia and industry, access to events, and research internships.

EFEQT is coordinated by the University of Strasbourg.

efeqt.eu →

2024–2028 €2.53M (EU) Coordinator

MLQ provides high-level interdisciplinary training to 10 doctoral researchers exploring how machine learning and quantum science feed into and benefit each other — extending machine-learning-based prediction of materials to strongly-correlated regimes, and accelerating quantum technologies through machine learning. The work has the potential to address some of the world’s most pressing challenges, from discovering greener chemical processes and more efficient materials to giving Europe an edge in the global tech race.

Funded by the European Union’s Horizon Europe research and innovation programme under Marie Skłodowska-Curie grant agreement number 101120240.

mlq.quantumexcellence.org →

5-year programme €13M total 51 PhD positions

Gen-Q is a five-year, EU-funded doctoral training programme coordinated by the Eucor European Campus, designed to prepare the next generation of experts in quantum science and technology. The programme offers 51 fully funded PhD positions across leading universities in France, Germany, Switzerland, the Netherlands, and Poland, combining international, interdisciplinary, and cross-sectoral experience.

Mission

Equip doctoral researchers with both technical expertise and transferable skills to become leaders in the European quantum ecosystem. Gen-Q creates a sustainable network for quantum science spanning physics, chemistry, materials science, and computer science.

Key features

• Each doctoral project runs for 48 months and leads to a PhD from one of the partner universities.
• Research topics focus on four major challenges in quantum technologies.
• Every PhD student has a personalised training committee and spends 3–12 months with a non-academic partner.
• Emphasis on scientific excellence, diversity, and gender equality.

€13M total: €6M from the EU, €2M from Switzerland, plus co-financing from all partner universities.

Eucor Gen-Q page →

2022–2026 €17.6M total €700k Strasbourg Local coordinator

DigiQ is reforming quantum technology education across Europe to meet the coming demand for a quantum-ready workforce. The programme develops 16 Quantum Technology master’s degrees, disseminates multilingual educational resources, and supports a pan-European student mobility scheme that doubles as a pipeline into the quantum industry.

Teaching

• Optics & Photonics (master course)
• Quantum Computing with Atoms and Light (doctoral course)
• Introduction to Quantum Science & Technology (master course)
• Open Quantum Systems (master course, co-taught)
• Many-body Physics (master course, co-taught)
• Quantum Computing, Rydberg Physics, Low-dimensional Physics (doctoral courses)

Projects

• QUSTEC (2019–2024, €1.1M, PI) — trinational MSCA COFUND doctoral programme in quantum science and technology. Steering committee member; the project supported 37 doctoral positions across the upper Rhine valley. Programme page →
• QMat (2018–2024) — coordinating team for the first Ecole Universitaire de Recherche in quantum science in France, an integrated MSc/PhD programme at the University of Strasbourg. qmat.unistra.fr →
• MOQS (2020–2024, €1.29M, PI) — H2020 MSCA Innovative Training Network on Molecular Quantum Simulations; trained 15 ESRs in quantum computing and simulations of molecular structure for the chemical and pharmaceutical industries. moqs.eu →