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Optimization and Novel insights: The convergence of Quantum Computing and Data Science in Engineering Application

Authors :

Nayereh Majd¹

1- تهران

Keywords :

Quantum computing،data science،engineering applications،optimization،quantum algorithms،machine learning،QAOA،VQE،quantum machine learning،predictive modeling

Abstract :

Abstract— The convergence of quantum computing and data science represents a transformative frontier in engineering applications, offering unprecedented capabilities for solving complex optimization problems and generating novel insights. This paper explores the synergistic potential of quantum algorithms and data-driven approaches to address challenges in fields such as supply chain logistics, structural design, energy systems, and predictive maintenance. Quantum computing, with its inherent parallelism and ability to handle high-dimensional spaces, enables exponential speedups in optimization tasks through algorithms like the Quantum Approximate Optimization Algorithm (QAOA) and variational quantum eigensolvers (VQE). Meanwhile, quantum machine learning techniques, such as quantum kernel methods and quantum neural networks, enhance pattern recognition and predictive modeling by leveraging quantum feature spaces. This review highlights key engineering applications where this convergence has demonstrated promise, including protein folding simulations, fluid dynamics optimization, and quantum-enhanced deep learning for material discovery. Despite being in its nascent stages, the integration of quantum computing with data science is poised to redefine computational limits, offering scalable solutions to problems previously deemed intractable. Challenges such as quantum decoherence, error mitigation, and hardware constraints are discussed, along with emerging strategies to overcome these barriers. The paper concludes by outlining future research directions aimed at harnessing the full potential of quantum-classical hybrid frameworks for engineering innovation.