Qiskit Variational Quantum Classifier on the Pulsar Classification Problem

2025/05/21 by Albert Schiaveto de Souza, Souza, Anna B. M., Clebson Cruz +3
Computer Science · #Computational Physics and Python Applications #FOS: Physical sciences #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2505.15600

openalex publication_date 2025/05/21 · openalex created_date 2025/10/19 · openalex updated_date 2026/07/28

Abstract

Quantum Machine Learning is a new computational tool that combines the quantum properties from quantum computing with the pattern recognition from machine learning. In this paper, we apply the Variational Quantum Classifier algorithm to the problem of pulsar classification of candidates from the High Time Resolution Universe 2 dataset. We use Qiskit Machine Learning circuits to compare the performance of the model using different feature selection methods, various number of features and training data size. Comparisons on the model from changing the data encoding and ansatz options are also reported. Keywords: Quantum Computing, Quantum Machine Learning, Astrophysics, Pulsars

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