About
Léa Cassé, PhD researcher (cotutelle University of Waikato × Institut Polytechnique de Paris) working on Quantum Machine Learning for data streams. Core focus is the spectral theory and practical design of Quantum Re-Uploading Units (QRU), with applications to time-series prediction and risk modelling.
Quantum Re-Uploading Units
Expressivity & trainability of QRU circuits, frequency/Fourier characterization, architecture design under NISQ constraints.
Learning on Data Streams
Online prediction and temporal modeling under drift, with a focus on deployable and robust methods.
Risk & Optimization
Hybrid QRU→QAOA pipelines, CVaR allocation, and decision-making under uncertainty for climate risk.
Featured publications
- Quantum Re-Upload Units: A Scalable and Expressive Approach for Time Series Learning — University of Waikato & École Polytechnique (IP Paris) (2024)
- Quantum Re-Uploading for Calorimetry: Optimized Architectures with Extended Expressivity — Artificial Intelligence Institute, University of Waikato & Institut Polytechnique de Paris (2024)