Gaspard Beugnot

Machine Learning & Optimization for quantum chip control

I'm currently a Machine Learning researcher at Alice & Bob, where I lead the Qubit Control team. Our goal is to achieve fault-tolerant quantum computing with cat qubits, and I design methods to extract the most performance out of our chip with a mix of learning, optimization, optimal control and RL.

I believe AI will be the cornerstone of a useful quantum computer, as it will be in biology and robotics. I love working in an interdisciplinary environment at the frontier of AI, experimental and theoretical physics.

I'm always open to external collaborations. Don't hesitate to reach out!

Before that I obtained a PhD from ENS and Inria in theoretical machine learning, under the supervision of Julien Mairal and Alessandro Rudi. My research focused on the interplay between generalization and optimization in learning algorithms, along with kernel sum-of-squares method for global optimization with certificates. Find my thesis' manuscript here!

And before that, I graduated from Ecole Polytechnique (X2016) and earned a master from École Normale Supérieure in Mathematics, Vision and Machine Learning in 2020 (Master MVA).