ISS-Sci - ISS LISA Science Group
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Conferences
Quantum Machine Learning – Integrating Hardware and Software Developments
13-19 Feb 2023
Max Planck Institute for the Science of Light, Erlangen, Germany
The Very-Long-Baseline Atom Interferometry
Workshop
13-14 Mar. 2023
CERN
The group leader, Dr. Laurentiu Caramete, and the PhD students, Maria Isfan and Florentina Pislan, represented the ISS-LISA Sci group at The Very-Long-Baseline Atom Interferometry Workshop. During the poster session, Maria discussed “On the Possibility of Implementing Quantum Algorithms Using the AION Cold Atoms System”, while Florentina presented the preliminary results of her study, “Multi-messenger studies involving gravitational waves”, conducted in the context of AION and AEDGE experiments.
Conference’s website
LISA Mission Board Meeting
05-08 June 2023
SRON Netherlands Institute for Space Research, Leiden, Netherlands
International Cosmic Ray Conference
26 July - 03 Aug 2023
Nagoya University, Nagoya, Japonia
During the poster session of the conference, PhD student, Alice Paun, presented the results of her study entitled “KM3NeT sensitivity to a flux of down-going nuclearites”, analysis conducted under the supervision of dr. Gabriela Pavalas and dr. Vlad Popa.
LISA Astrophysics Working Group Meeting
13-15 Sep 2023
University of Milano-Biccoca , Milan, Italy
On the second day of the conference, PhD student Florentina Pislan discussed on “The Impact of Gravitational Waves on Multi-Messenger Analysis of Observed Electromagnetic Sources”, study conducted under the supervision of dr. Ana Caramete and dr. Laurentiu Caramete.
IEEE Quantum Week
17-22 Sep 2023
Bellevue, Washington Hyatt Regency Bellevue on Seattle's Eastside
PhD student, Maria Isfan, held an oral presentation withing the Quantum AI Workshop with the title “Classification of Gravitational Waves Using Neural Networks on Quantum Computers”. In this study, her, dr. Ana Caramete and dr. Laurentiu Caramete evaluate the feasibility of using quantum neural networks for classifying gravitational waveforms, using both simulators and quantum computers. The analysis is quite interdisciplinary in its nature, combining knowledge involving astrophysics, quantum information as well as quantum and classical machine learning. On one hand, we showed that the quantum classifiers and hybrid classical-quantum neural networks give maximally accurate results when tested on a simple dataset and ran on a simulator. On the other hand, we showed that quantum neural networks (ran on a simulator) can distinguish with high accuracy between noisy gravitational waves and noise, for LISA space mission specific simulated data. Moreover, we showed that adding a quantum layer to poorly performing classical neural network can highly improve its accuracy. When running any of our quantum algorithms on a real quantum computer, error minimizing algorithms need to be implemented in order to obtain a satisfying accuracy.
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