Léa Cassé

PhD Candidate — Quantum Machine Learning for Data Streams
University of Waikato (NZ) & École Polytechnique / IP Paris (France)

📍 Christchurch, New Zealand
📧 casse.lea@gmail.com 🌐 github.com/LeaCasse 🔗 LinkedIn


🎯 Professional Summary

I design and analyze variational quantum models for time-series forecasting and decision-making in streaming contexts.
My research focuses on Quantum Re-Uploading Units (QRU) and Quantum Residual Blocks (QRB), combining Fourier analysis, gradient studies, and trainability metrics to characterize expressivity in shallow circuits.
I developed a World Bank GIC-winning QRU prototype for flood-risk forecasting, co-authored a reinforcement-learning study on bus-load regulation, and wrote a preprint on calorimetry optimization using QRU architectures.
My technical work relies on PennyLane, Qiskit, PyTorch, and qBraid for reproducible hybrid quantum-classical experiments.


🧠 Core Skills

Quantum ML & Computing
QRU/QRB architectures · Variational circuits · Parameter encoding · QAOA · Fourier/spectral analysis · QSVT · Coherent amplitude/phase estimation · PennyLane · Qiskit

Machine Learning & Data
PyTorch · scikit-learn · Time-series / streaming learning · Reinforcement Learning (policy & value) · Model evaluation metrics

Engineering & Tools
Python · NumPy/pandas · Experiment tracking · qBraid · Git · Basic HPC/GPU workflows

Languages
French (C2) · English (C1) · Spanish (B1)


🔬 Experience & Research

2024 – 2027 · PhD in Quantum ML for Data Streams

University of Waikato — École Polytechnique (IP Paris / LLR)
Supervisors : Prof. Albert Bifet, Prof. Bernhard Pfahringer, Dr. Frédéric Magniette

2015 – 2023 · Projects & Internships


📝 Publications & Preprints


👩‍🏫 Teaching & Outreach


🎓 Education


🏆 Awards & Achievements


Full CV (PDF) : Download CV Léa Cassé