About me
Quantum models for dynamic data, built with a physicist’s perspective
I am a PhD candidate in Quantum Machine Learning for Data Streams, conducting a joint PhD between the University of Waikato in New Zealand and École Polytechnique / Institut Polytechnique de Paris in France. My research is supervised by Prof. Albert Bifet and Prof. Bernhard Pfahringer.
My work lies at the intersection of quantum information, machine learning, and dynamic data. I study how shallow variational circuits can represent and learn temporal structure, with a particular focus on Quantum Re-Uploading Units (QRUs), spectral/Fourier diagnostics, trainability, and deployment under NISQ constraints.
Research focus
Three directions within one PhD programme
QRU applications
Particle identification, time-series forecasting, flood-risk prediction, and compact quantum models for data streams.
QRU theory
Frequency structure, expressivity, gradients, parameter sharing, and the relationship between circuit architecture and target spectra.
Forecast-to-decision pipelines
Coupling quantum forecasting with risk scoring, CVaR objectives, QUBO formulations, and QAOA-based optimisation.
Applied AI/ML work
Research-grade engineering beyond the model
I also work as a freelance AI/ML consultant. My applied work includes governed data contracts, forecasting evaluation, provenance and audit logic, explainability hooks, human-in-the-loop review, and reproducible Python/FastAPI-style prototypes. I also collaborate on explainable AI for cross-day EMG gesture classification, with an emphasis on separating physiologically meaningful signal regions from unstable artefacts.
Explore ML FreelanceBackground
From fundamental physics to quantum machine learning
Before starting my PhD, I studied fundamental and quantum physics in Toulouse and Montpellier. My earlier research experience includes NV centres in diamond, Bell inequalities, quantum chaos, and quantum-computing projects.
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