Nicolas Brunner

Nicolas Brunner is a quantum physicist at the University of Geneva who has coauthored neural-network tools for studying quantum nonlocality and models of computing built on quantum thermal machines. After a doctorate under Nicolas Gisin in 2007 and a postdoctoral fellowship at the University of Bristol, he became SNSF Professor at Geneva in 2012. He coauthored the 2013 review Bell nonlocality with Daniel Cavalcanti, Stefano Pironio, Valerio Scarani and Stephanie Wehner, and in 2019 a neural network oracle for testing nonlocality in quantum networks. His 2023 paper on the thermodynamic neuron, written with Patryk Lipka-Bartosik and Martí Perarnau-Llobet, appeared in Science Advances in 2024, and in 2026 he coauthored a neural-network toolbox for Bell locality and the Thermodynamic Networks framework, which classifies MNIST digits. He is a professor at the University of Geneva, and in October 2025 he explained the Nobel Prize in Physics awarded to John Clarke, John Martinis and Michel Devoret on the RTS program Forum.

Now at: University of Geneva
Born: 1979

Years link to their sources.

Timeline

2007

Doctorate under Nicolas Gisin

He earns his Ph.D. from the University of Geneva in 2007 with the thesis Quantum non-locality: Fundamentals and Applications in Quantum Information Science, directed by Nicolas Gisin.
Since 2012

SNSF Professor at Geneva

After a postdoctoral fellowship at the University of Bristol, he becomes SNSF Professor in the Department of Theoretical Physics at the University of Geneva in 2012. His research covers the foundations of quantum mechanics, quantum information, and quantum thermodynamics.
2013–2014

The Bell nonlocality review

In 2013, he coauthors the review Bell nonlocality with Daniel Cavalcanti, Stefano Pironio, Valerio Scarani and Stephanie Wehner, published in Reviews of Modern Physics in 2014. The review surveys the main concepts and tools used to describe and study the nonlocality of quantum theory.
2019–2020

A neural network oracle

In 2019, he coauthors a paper with Tamás Kriváchy, Yu Cai, Daniel Cavalcanti, Arash Tavakoli and Nicolas Gisin that uses neural networks to learn the classical strategies that reproduce a distribution from a quantum network. The paper appears in npj Quantum Information in 2020 and applies the method to the triangle network.
2021

Neural networks and entanglement

In 2021, he coauthors a method with Antoine Girardin and Tamás Kriváchy that parametrizes separable quantum states with a neural network and trains it to minimize the distance to a target state. The output gives an upper bound on the entanglement of the target state and an approximation of its closest separable state.
2023–2024

The thermodynamic neuron

In 2023, he coauthors with Patryk Lipka-Bartosik and Martí Perarnau-Llobet a model of computing with autonomous quantum thermal machines, in which a thermodynamic neuron implements any linearly-separable function. The paper appears in Science Advances in 2024.
October 2025

Explaining the quantum Nobel

As a professor at the Faculty of Science of the University of Geneva, he explains on the RTS program Forum in October 2025 the challenges behind the work of John Clarke, John Martinis and Michel Devoret, awarded the Nobel Prize in Physics.
2026

Toolbox and thermodynamic networks

In March 2026, he coauthors with Antoine Girardin, Mohammad Massi Rashidi, Géraldine Haack and Alejandro Pozas-Kerstjens a software toolbox that parameterizes local models in quantum networks with neural networks, published in Quantum Science and Technology in 2026. In May 2026, he coauthors with Patryk Lipka-Bartosik, Martí Perarnau-Llobet and others the paper Thermodynamic Networks, a framework for physics-based computation that reaches high performance on MNIST digit classification.