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.

JA in collaboration with Frank Schäfer, Heinz Krummenacher, Stefan Kehrein, and Christoph Bruder

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.

JA in collaboration with Flemming Holtorf, Frank Schäfer, Christopher Rackauckas, and Alan Edelman

Performance bounds for quantum control

Machine learning phases of matter

Using machine learning techniques to detect phase transitions autonomously from readily accessible data.

JA in collaboration with Frank Schäfer, Niels Lörch, Flemming Holtorf, Christoph Bruder, Alan Edelman, Martin Žonda, and Axel Lode

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.

JA in collaboration with Debasish Koner, Juan Carlos San Vicente Veliz, Narendra Singh, Raymond Bemish, Silvan Käser, and Markus Meuwly

Machine learning molecular collisions

Entropy production in ticking clocks

Studying the relation between the entropy production per tick of ticking clocks and their accuracy.

JA in collaboration with Mischa Woods, Christoph Bruder, and Renato Renner

Entropy production in ticking clocks