DATA ANALYSIS FOR GRAVITATIONAL WAVES USING NEURAL NETWORKS ON QUANTUM COMPUTERS

In this study, we 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. We showed that the quantum classifiers and hybrid classical-quantum layers give highly accurate results when tested on a simple dataset and ran on a simulator; also, adding a quantum layer to poorly performing classical neural network can highly improve its accuracy. When running on a real quantum computer, error minimizing algorithms need to be implemented in order to obtain a satisfying accuracy.