Quantum Computing Transforms Human Resource Analysis in the Maritime Sector in Operational Efficiency and Occupational Safety

Abstract

This research aims to examine the application of quantum computing in maritime HR analysis thru a hybrid quantum-classical approach for optimizing Human Resource Analysis Efficiency, Challenges in Implementing Quantum Computing, Decision-Making Processes, and Implications for Workforce Skills. The novelty of this research lies in the integration of quantum algorithms such as the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) into the maritime human resource management model, which was previously dominated by classical machine learning-based methods. The research method uses an experimental quantitative design with simulations based on operational datasets of merchant ships, covering variables such as Human Resource Analysis, Operational Efficiency, and Occupational Safety. The quantum computing model was compared with classical models (linear programming and deep learning) in terms of prediction accuracy, computational efficiency, and optimization solution quality. The research results show that the quantum computing-based approach can improve HR analysis efficiency by 16,1% compared to classical methods, and reduce the challenges in implementing Quantum Computing by up to 25% thru more optimal workload distribution. Additionally, quantum-based predictive models increase decision-making process accuracy by 28,7%, and the implications for workforce skills by 33,7%, especially under extreme operational conditions. Other findings indicate that the use of the QAOA algorithm can reduce computational complexity in large-scale combinatorial optimization problems, which are common in maritime HR management.