2021/12/05 by Mohammad Kordzanganeh, Kordzanganeh, Mohammad, Aydin Utting +3 · 2 citations
Computer Science · Engineering · #FOS: Computer and information sciences #FOS: Physical sciences #High Energy Astrophysical Phenomena (astro-ph.HE) #Machine Learning (stat.ML) #Neural Networks and Reservoir Computing #Optical Network Technologies #Quantum Computing Algorithms and Architecture #Quantum Physics (quant-ph)
paper · pdf · doi:10.48550/arxiv.2112.02655
openalex publication_date 2021/12/05 · openalex created_date 2022/05/05 · openalex updated_date 2026/07/28
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.