Author(s): Vimal Pradeep Venugopal

Abstract: Quantum computing represents a paradigm shift in computational capability that promises to transform data science by addressing problems currently intractable on classical systems. As global data creation expands exponentially, quantum technologies emerge as potential solutions for managing unprecedented complexity. This article examines the fundamental advantages of quantum computing, including exponential parallelism through superposition, management of computational complexity, and the ability to solve previously unsolvable problems. High-impact application domains are explored, including machine learning transformation, optimization at unprecedented scales, cryptography and data security, molecular simulation for drug discovery, and complex systems modeling for climate prediction. While these opportunities appear transformative, substantial technical challenges remain, including quantum decoherence, hardware scalability constraints, and algorithmic development gaps. The path forward likely involves hybrid quantum-classical architectures, targeted applications in big data analytics, enhanced AI systems, and revolutionary simulation capabilities for complex systems.

Keywords: Quantum computing, data science, superposition, computational complexity, hybrid quantum-classical architectures

Article Info: Received: 04 Jul 2025; Received in revised form: 28 Jul 2025; Accepted: 02 Aug 2025; Available online: 08 Aug 2025

DOI: 10.22161/ijels.104.52