https://publikasi.dinus.ac.id/index.php/jcta <p>Journal of Computing Theories and Applications (JCTA) ISSN: <a href="https://portal.issn.org/resource/ISSN/3024-9104">3024-9104</a> is an international journal that covers all aspects of foundations, theories, and the practical applications of computer science. <strong>FREE OF CHARGES </strong>for submission and publication. All accepted articles will be published online, get <strong>DOI CROSSREF</strong> and <strong>OPEN ACCESS</strong>. The rapid peer-reviewed process takes approximately two weeks to get the first decision. </p> Universitas Dian Nuswantoro en-US Journal of Computing Theories and Applications 3024-9104 https://publikasi.dinus.ac.id/index.php/jcta/article/view/10424 <p>This work explores the potential of PennyLane and variational quantum-classical algorithms (VQCA) to forecast lung cancer using a structured dataset. The VQCA model performs exceptionally well, with flawless training, validation, and test accuracies of 1.0, demonstrating its capacity to identify patterns in the dataset and provide reliable predictions successfully. Contrarily, the accuracy of the quantum neural network (QNN) and classical neural network (NN) models is lower, demonstrating the benefits of utilizing quantum computing methods for enhanced predictive modeling. We provide a complete examination of the data, stressing the better performance of the VQCA model and its promise in correctly predicting lung cancer. The results highlight the importance of quantum-classical algorithms and help us understand the benefits and drawbacks of various strategies for predicting lung cancer. The study highlights the potential applications of quantum computing techniques in advancing the field of healthcare analytics. It shows the capability of the VQCA model to predict lung cancer using a tabular dataset accurately. Further research in this area is needed to explore scalability and practical implementation aspects. In summary, this study showcases the potential of VQCA and PennyLane in predicting lung cancer and underscores the benefits of quantum computing techniques in healthcare analytics.</p> Philip Omoniyi Adebayo Frederick Basaky Edgar Osaghae Copyright (c) 2024 Philip Omoniyi Adebayo, Frederick Basaky, Edgar Osaghae3 https://creativecommons.org/licenses/by/4.0 2024-12-06 2024-12-06 2 3 307 323 10.62411/jcta.10424