Journal of Artificial Intelligence and Metaheuristics
Journal DOI
2833-5597ISSN (Online)
Volume 10 , Issue 2 , PP: 67-81, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Ahmed Hamid Elias 1 * , Dhurgham Abbas Mohsin Albojwaid 2 , Ahmed younus abdulkadhim 3 * , Raad S. Alhumaima 4 , Laith Farhan 5
Doi: https://doi.org/10.54216/JAIM.100205
Integration of quantum-inspired algorithms in machine learning has opened up new horizons for improving predictive performance, efficiency, and scalability across a broad spectrum of application domains. This paper presents a comparative investigation between traditional machine learning techniques and quantum-inspired models. Experimental experiments demonstrate that quantum-inspired approaches exhibit higher accuracy, training effectiveness, and stability on difficult datasets than traditional methods. Results point towards higher convergence rates, shorter runtime, and enhanced generalization capacity in quantum-inspired models, realized in the form of enhanced accuracy, precision, recall, and F1-scores. Receiver operating characteristic (ROC) and precision–recall analyses further confirm the superior discriminative power of quantum-inspired approaches. Results point toward the potential of quantum-inspired machine learning as an interface between conventional algorithms and the new frontier of quantum computing with a stepping stone to future-proof intelligent systems.
Quantum-Inspired Algorithms , Machine Learning , Quantum Computing , Predictive Analytics , Model Optimization , Hybrid Frameworks , Performance Metrics , Artificial Intelligence
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