Julian Arnold

Julian Arnold

Physics PhD

Quantum Theory Group @ University of Basel

About me

I am a PostDoc (graduated in March 2025) in the quantum theory group of Prof. Christoph Bruder at the University of Basel. My research focuses on using machine learning to solve problems in quantum physics.

Interests
  • Machine learning
  • Estimation theory
  • Stochastic processes
  • Quantum information theory
  • Many-body physics
  • Chemical reaction networks
  • Nonlinear dynamics
Education

  • PhD in Theoretical Physics, 2021 - 2025

    Department of Physics, University of Basel

  • MSc in Nanosciences, 2019 - 2021

    Swiss Nanoscience Institute, University of Basel

  • Master's thesis under the supervision of Dr. Mischa Woods and Prof. Renato Renner, 2020 - 2021

    Institute for Theoretical Physics, ETH Zurich

  • BSc in Nanosciences, 2016 - 2019

    Swiss Nanoscience Institute, University of Basel

Research projects

Differentiable isospectral flows for matrix diagonalization
Implementing continuous isospectral flows in a differentiable manner to compute derivatives efficiently and accurately, enabling the solution of inverse problems.
Short talk at JuliaCon (2023)
Differentiable isospectral flows for matrix diagonalization
Performance bounds for quantum control
Bounding the best attainable performance for quantum control problems using moment-sum-of-squares techniques.
2024 IEEE TAC paper Short talk at JuliaCon by F. Holtorf (2024 IEEE TAC) Poster (2024 IEEE TAC) Poster (paper in preparation, open-loop control)
Performance bounds for quantum control
Machine learning phases of matter
Using machine learning techniques to detect phase transitions autonomously from readily accessible data.
2021 PRRes paper Poster (2021 PRRes) 2022 PRX paper Press release (2022 PRX) Poster (2022 PRX) Short talk (2022 PRX) Long talk (2022 PRX) 2024 PRL paper Press release UniBas (2024 PRL) Press release MIT (2024 PRL) Short talk (2024 PRL) Long talk (2024 PRL) 2023 NeurIPS ML4Physical Sciences Workshop paper Poster (2023 NeurIPS Workshop) 2023 arXiv preprint (Fisher) Poster (2023 arXiv) 2024 arXiv preprint (LLM) Poster (2024 arXiv, LLM) 2024 arXiv preprint
Machine learning phases of matter
Machine learning molecular collisions
Modelling changes in energy and state population distributions of molecular systems undergoing collisions using machine learning methods.
2020 JPCA paper 2022 JCP paper 2022 JPCA paper
Machine learning molecular collisions
Entropy production in ticking clocks
Studying the relation between the entropy production per tick of ticking clocks and their accuracy.
Master thesis Poster
Entropy production in ticking clocks