Julien Baglio is a Senior Research Associate with responsibilities similar to Associate Professor at the Center for Quantum Computing and Quantum Coherence (QC2) at the University of Basel, as well as a Quantum Algorithms Researcher at QuantumBasel. He earned in 2008 an MSc in theoretical physics from the École Normale Supérieure in Paris. He holds a PhD in theoretical particle physics from the University of Paris-Sud 11, obtained in 2011, with a focus on the study of the infamous Higgs boson both in the Standard Model and in its supersymmetric extensions. His work played an important role in its discovery, which was awarded the Nobel Prize in physics in 2013. He then worked as postdoctoral researcher at the Karlsruhe Institute of Technology (2011-2014) and as senior postdoctoral researcher at the University of Tübingen (2014-2019) before joining the Theory Department at CERN as a Senior Research Fellow (2019-2023). Since 2023 he has been working at QuantumBasel and since 2024 at the University of Basel, first as a guest researcher and since 2025 as a Senior Research Associate.

After a fruitful career in particle physics with particular emphasis on QCD higher-order calculations and Higgs physics, his research focuses now on quantum machine learning, in particular quantum generative modelling, as well as quantum Hamiltonian simulations, with a particular emphasis on implementation on actual quantum hardware from IBM or IonQ.

My main focus is on quantum algorithms, mainly (but not only) for healthcare and life sciences applications. With my collaborators I explore:

• Quantum generative models such as quantum generative adversarial networks, to enhance expressivity and trainability for drug discovery applications and data augmentation;
• Quantum simulations for NMR spectroscopy, both with quantum artificial intelligence and quantum (Hamiltonian) simulations;
• Quantum reservoir computing techniques, in particular for time series forecasting such as for wildfire prediction;
• Quantum optimization algorithms such as quantum approximate optimization algorithms (QAOA) and its variants, as well as quantum annealing approaches (applications for molecular docking, transport network, production scheduling, etc.).

A key aspect of our investigations is also to run our new algorithmic designs on actual quantum computing systems such as IBM superconducting-qubit computers or IonQ trapped-ion quantum computers, in order to compare performances and benchmark the end-user applications.