I’m Tom Magorsch, I’m a physicist mainly interested in Particle Phenomenology, and the application of Machine Learning and Quantum Computing to HEP problems.
  • Currently I am at the TU Munich working on non-equilibrium physics in heavy ion collisions and new computational techniques to treat them.
  • Furthermore, I am part of the ML4SCI-Organization, where I participate in the Google Summer of Code program.
  • In this blog I will document my GSoC project and maybe write about some other interests.

Quantum GANs

This year I’m participating in the Google Summer of Code again. Just like last year I’m working with the ML4SCI organization. In this years project I am working on Quantum Generative Adversarial Networks. GANs Generative Adversarial Networks (GANs) are a class of unsupervised machine learning models proposed in ( Citation: Goodfellow, Pouget-Abadie & al., 2014 Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A. & Bengio, Y. (2014). Generative Adversarial Networks....

July 29, 2023 · 9 min · Tom Magorsch

NERSC Open Hackathon 2022 | Multi-GPU quantum circuit simulation in Pennylane

The past month I have been participating in the NERSC Open Hackathon hosted together with NVIDIA. Throughout the event we had access to the Perlmutter compute system and worked together with mentors on scaling our scientific software projects on GPUs. During the event I worked on scaling the training of VQCs in Pennylane to multiple GPUs. A word of thanks goes to the organizers and all the mentors who helped us throghout the event....

December 18, 2022 · 5 min · Tom Magorsch

GSoC 22 | Quantum Autoencoders for HEP Analysis at the LHC

This is a summary of my 2022 GSoC project with ML4SCI. The ML4SCI organization accustoms different projects of machine learning applied to scientific problems, many connected to high-energy physics. A big thank you to Sergei Gleyzer for the supervision and support. Abstract The Standard Model of particle physics is a theory that describes the fundamental particles and the interactions between them. While it has extensively been tested and was able to correctly predict experiments to an impressive degree, there are multiple reasons to believe that it cannot be a complete description of nature....

September 21, 2022 · 16 min · Tom Magorsch

Data re-uploading

An important motivation for deep learning was the Universal Approximation Theorem which shows, that neural networks can theoretically approximate any function. When it comes to quantum machine learning, a similar statement can be made. Surprisingly a single qubit is sufficient, to perform the classification of arbitrary data distributions. Universal Approximation Theorem The Universal Approximation Theorem (there are many versions with different constraints) states that the functions which can be expressed by a neural network with a single hidden layer and arbitrarily many units are dense in the space of continuous functions....

September 15, 2022 · 8 min · Tom Magorsch

Quantum Natural Gradient Descent

When training Variational Quantum Algorithms we aim to find a point in the parameter space that minimizes a particular cost function, just like in the case of classical deep learning. Using the parameter-shift rule, we are able to compute the gradient of a Parametrized Quantum Circuit (PQC) and can therefore use that gradient descent method proven in classical machine learning. However vanilla gradient descent can face difficulties in practical training which can be circumvented with Quantum Natural Gradient Descent (QNG)....

August 27, 2022 · 7 min · Tom Magorsch