Quantum-Inspired Machine Learning: Bridging Classical and Quantum Algorithms Ahmed Hamid Elias1,*, Dhurgham Abbas Mohsin Albojwaid2, Ahmed younus abdulkadhim3, Raad S. Alhumaima4, Laith Farhan51College of Health and Medical Techniques, Al-Furat Al-Awsat Technical University, Najaf, Iraq 2Jabir Ibn Hayyan University for Medical and Pharmaceutical Sciences, Central Library Automated Systems Department, Najaf, Iraq3College of Health and Medical Techniques, Al-Furat Al-Awsat Technical University, Najaf, Iraq 4Brunel University, Uxbridge UB8 3PH, U. K 5School of Engineering, Manchester Metropolitan University, Manchester, M1, UK Emails: ahmed.elias@atu.edu.iq; Dhurgham.a.mohsin@jmu.edu.iq; ahmed.kazem.chm@atu.edu.iq; 1234914@alumni.brunel.ac.uk; l.al-bayati@mmu.ac.uk
Abstract 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. Keywords: Quantum-Inspired Algorithms; Machine Learning; Quantum Computing; Predictive Analytics; Model Optimization; Hybrid Frameworks; Performance Metrics; Artificial Intelligence