Quantum-inspired algorithm for direct multi-class classification

Over the last few decades, quantum machine learning has emerged as a groundbreaking discipline. Harnessing peculiarities of computation for tasks offers promising advantages. Quantum-inspired revealed how relevant benefits problems can be obtained using information theory even without employing computers. In recent past, experiments have demonstrated to design an algorithm binary classification inspired by method state discrimination, which exhibits high performance with respect several standard classifiers. However, generalization this quantum-inspired classifier multi-class scenario remains nontrivial. Typically, simple solution in decomposes into combinatorial number classifications, concomitant increase computational resources. study, we introduce that avoids problem. Inspired our performs directly We first compared eleven The comparison excellent classifier. Comparing these results those decomposition classifiers shows improves accuracy and reduces time complexity. Therefore, proposed work is effective efficient framework classification. Finally, although advantages attained any component hardware, discuss it possible implement model hardware.