I’m Tom Magorsch, a physicist interested in particle phenomenology, machine learning and software development.
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....
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....
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)....
In my GSoC project, I explore the use of Quantum Autoencoders for the analysis of LHC data. Autoencoders are an unsupervised learning technique, which learns a smaller latent representation of data. The quantum analog of a classical autoencoder equally aims to learn a smaller representation of data. A naive Quantum Autoencoder My first idea for a Quantum circuit closely follows the architecture of a classical autoencoder. The structure of the circuit is conceptually sketched in the following figure....