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....
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....
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....