Optimising Pulsar Classification in Astronomy through Quantum-Assisted Approach

Pulsar stars, characterized by their highly magnetized and rapidly rotating compact nature, as well as their emission of radiation beams from magnetic poles, play a crucial role in astrophysics, particularly in the study of gravitational waves and general relativity. This paper investigates the application of machine learning techniques for the classification of pulsars using radio wave emission data from the HTRU-2 dataset, which contains an extensive collection of candidate samples. The study explores two quantum-assisted methodologies: a multi-qubit Quantum Approximate Optimization Algorithm (QAOA) inspired encoding and an innovative single-qubit Quantum Asymptotically Universal Multi-feature (QAUM) encoding within a quantum neural network framework. The performance evaluation encompasses a range of metrics, including accuracy, precision, recall, and specificity, which are analyzed across various optimizer configurations and learning rates. Notably, the QAUM approach consistently demonstrates superior sensitivity and reduced error rates compared to QAOA, particularly evident at specific learning rate settings. These findings underscore the transformative potential of quantum computing in advancing pulsar detection methodologies, highlighting its efficacy and implications for astrophysical data analysis and the broader field of quantum-enhanced scientific exploration.