In this work we introduce a novel approach to the pulsar classification problem in time-domain radio astronomy using a Born machine, often referred to as a quantum neural network. Using a single-qubit architecture, we show that the pulsar classification problem maps well to the Bloch sphere and that comparable accuracies to more classical machine learning approaches are achievable. We introduce a novel single-qubit encoding for the pulsar data used in this work and show that this performs comparably to a multi-qubit QAOA encoding.
Cite
@article{arxiv.2112.02655,
title = {Quantum Machine Learning for Radio Astronomy},
author = {Mohammad Kordzanganeh and Aydin Utting and Anna Scaife},
journal= {arXiv preprint arXiv:2112.02655},
year = {2021}
}
Comments
Accepted in: Fourth Workshop on Machine Learning and the Physical Sciences (35th Conference on Neural Information Processing Systems; NeurIPS2021); final version