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We recently put out a paper on bottomonium suppression in the quark gluon plasma 2403.15545. This is a project I’ve been working on for some time now and I want to show real quick what we have been doing. Quarkonium suppression Heavy ion collisions are experiments where two heavy nuclei are collided. Such experiments are conducted e.g. at CERN. In these collisions a state of matter called the quark gluon plasma is created....

This is a summary of my 2023 GSoC project with the ML4SCI-organization. In my project I designed and implemented a Quantum Generative Adversarial Network for the generation of HEP experiment data. The full code for all my work can be found on Github. In the following post I will outline my work and describe some parts of the implementation and the results Event generation in HEP experiments In high energy physics experiements like they are conducted at CERN, an integral part of the analysis process is the comparison of measurements with results expected based on predictions from our theory of nature, the Standard Model of particle physics....

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....

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....

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....