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

Quantum Autoencoder

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....

July 12, 2022 · 7 min · Tom Magorsch

Hello World! | Hello GSoC!

This year I’m participating in the Google Summer of Code with the ML4SCI organization. My project proposal deals with a quantum variational autoencoder (QVAE) for the anaysis of particle physics data. Such unsupervised learning paradigms can be used to search for new physics in a model-agnostic way ( Citation: Kasieczka, Nachman & al., 2021 Kasieczka, G., Nachman, B. & al. (2021). The LHC Olympics 2020: A Community Challenge for Anomaly Detection in High Energy Physics....

June 12, 2022 · 1 min · Tom Magorsch