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I'm a computational-quantum-physicist, holding the chair of Artificial Intelligence and Quantum Physics at the Center for Theoretical Physics of Ecole Polytechnique, around Paris.

I study how to exploit Machine Learning techniques to address Quantum-physical problems, in particular with Neural Quantum States. Given my PhD was in Quantum Optics, I am more interested in complex dissipative, non-equilibrium systems rather than (boring) equilibrium ones. This makes me keen on techniques for encoding symmetries and structure in a neural network, a tough task.

I also try to apply my classical-optimisation knowledge to develop new variational quantum algorithms to simulate quantum systems. On quantum systems. Ain't that funny?

In social circles I pretend I don't understand what MLIR is and hide my nerdiness, but at times I can get hardcore and read assembly. Though, I'd rather not. I'd rather write qasm and embed it in LLVMIR.

I am leading the development of NetKet, a machine learning toolbox for many-body quantum physics. It's a Python package based on Jax. But I'd love to see it written in Julia one day. To ease the pain that writing Python causes me, I contribute to plum, a multiple-dispatch Julia-like system for python.