Julian Arnold

Julian Arnold

PhD Candidate

Quantum Theory Group @ University of Basel

About me

I am a PhD candidate in the quantum theory group of Prof. Christoph Bruder within the “Quantum Computing and Quantum Technology” PhD school at the Department of Physics of 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

  • 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 feedback control problems using moment-sum-of-squares techniques.
arXiv preprint Short talk at JuliaCon by F. Holtorf (2023) Poster
Performance bounds for quantum control
Machine learning phases of matter
Using machine learning techniques to detect phase transitions autonomously from readily accessible data.
Code PRResearch paper PRX paper Short talk at AMLD (2022) Poster #1 Poster #2 Press release Long talk at PIQuIL (2023) arXiv paper Short talk at JuliaCon (2023)
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.
Code JPCA paper #1 JCP paper JPCA paper #2
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