Speaker
Description
Quantum computing and machine learning are two cutting-edge domains, in continuous evolution. Their intersection is quantum machine learning, an area with great potential of enhanced data analysis due to quantum advantage. As gravitational waves astronomy is progressing rapidly, the need for fast and robust data analysis tools increases.
In our work, we present a quantum neural network data analysis pipeline for detection of gravitational waves signals, in the context of LISA Space Mission. We analyze the mock dataset Sangria v2, consisting of massive black hole binaries signals, provided by the LISA Consortium. We do this analysis in two steps: first, we design and train a variational classifier based quantum neural network for separation of gravitational waves signals from noise. Then, we search for the coalescence times of the signals in the blind dataset. We compare our quantum neural network approach with the classical neural network approach developed in our group, and we highligh the main quantum advantages. Due to current technological limitations, we use for now simulated quantum computer ecosystems.
In the future, we will addapt our quantum neural network data analysis pipeline to the recent LISA mock dataset, Mojito, and benchmark it against quantum and classical machine learning architectures.