Quantum machine learning studies the intersection between machine learning and quantum information theory, focusing both on how quantum computation can speed-up tasks typically handle by machine learning, and how classical machine learning can further our understanding of quantum information science.
It is a vast interdisciplinary field that encompasses many different lines of study. We focus in particular on the study of quantum extreme learning machines and quantum reservoir computers, a class of quantum machine learning techniques that promises great potential, while also remaining a simple enough model to allow reaching many theoretical insights into what might make quantum speed-ups at all possible.
People
Group members with at least two papers in this research line
Projects
Recent publications 19
All 19 papers in machine learning- Who can sample forever? Shot noise effects in quantum linear regressionPreprint Preprint, arXiv:2610.12015 (2026)
- Triple descent in quantum linear regressionPreprint Preprint, arXiv:2611.00272 (2026)
- Machine Learning-Based Characterisation of the Non-Markovian Dynamics of a Nitrogen-Vacancy CentrePreprint Preprint, arXiv:2608.27632 (2026)
- Exoplanetary atmospheres retrieval via a quantum extreme learning machineQuantum Machine Intelligence 8, 69 (2026)
- Efficient classical training of model-free quantum photonic reservoirPreprint Preprint, arXiv:2604.12441 (2026)
- Quantum reservoir computing for photonic entanglement witnessingScience Advances 11, eady7987 (2025)
- Detection of noise correlations in two qubit systems by Machine LearningPreprint Preprint, arXiv:2509.03389 (2025)
- A machine learning based approach to the identification of spectral densities in quantum open systemsThe European Physical Journal Special Topics 235, 2977–2989 (2025)