Quantum Natural Gradient Based Collaborative Task and Systems Allocation Approach for Heterogeneous Multi-Unmanned Vehicles

Authors: T. Navya Deepthi, G. Krishnaveni, T. Sarika, M. Mounika, S. Tejaswini

Abstract: Task allocation is a critical component of various systems, requiring efficient and accurate assignment of tasks to resources. Existing models often struggle with balancing precision and recall, leading to suboptimal performance. The limitations of existing task allocation models, including low precision and recall, hinder the efficiency and effectiveness of task allocation processes. Current models often rely on simplistic approaches, failing to account for complex task dependencies and resource constraints. This leads to reduced accuracy and increased errors in task allocation. The Quantum Natural Gradient Based Collaborative Task and Systems Allocation Approach for Heterogeneous Multi-Unmanned Vehicles framework addresses these limitations by leveraging advanced algorithms and techniques to optimize task allocation. By integrating precision and recall metrics, Quantum Natural Gradient Based Collaborative Task and Systems Allocation Approach for Heterogeneous Multi-Unmanned Vehicles ensures a balanced approach to task allocation, minimizing errors and maximizing efficiency. Experimental results demonstrate the effectiveness of the Quantum Natural Gradient Based Collaborative Task And Systems Allocation Approach For Heterogeneous Multi-Unmanned Vehicles framework, achieving a precision of 96%, recall of 95%, and F1 score of 95.5%. The framework also exhibits a low task allocation time of 2.0 seconds and high pre-processing accuracy of 98%.

DOI: http://doi.org/10.5281/zenodo.20390184