Abstract

Quantum Computing (QC) and Machine Learning (ML) are two groundbreaking fields that, when combined, offer the potential to revolutionize problem-solving across disciplines. Quantum Machine Learning (QML) leverages quantum mechanics—such as superposition and entanglement—to accelerate and scale classical learning models beyond the current computational limits. This paper provides a beginner-friendly review of QML, introducing core quantum principles, surveying popular quantum algorithms applicable to ML, and presenting a comparative analysis of classical versus quantum paradigms. The study reviews hybrid quantum-classical approaches—particularly Variational Quantum Circuits (VQCs)—tailored for current Noisy Intermediate-Scale Quantum (NISQ) devices. It also includes an application demo using Qiskit and PennyLane to build a quantum-classical pipeline for handwritten digit classification using a subset of MNIST. Each section includes tables summarizing concepts, complexity comparisons, and future directions. Key limitations such as decoherence, quantum error correction, and data encoding strategies are also discussed. The goal is to demystify QML for students, researchers, and developers, encouraging deeper exploration. References include recent peer-reviewed publications and arXiv preprints with valid DOIs, ensuring the reliability and relevance of the material.

References

1. Cerezo, M. et al. (2021). Variational Quantum Algorithms. Nature Reviews Physics. DOI: 10.1038/s42254-020-0232-6
2. Harrow, A. W., Hassidim, A., & Lloyd, S. (2009). Quantum Algorithm for Linear Systems of Equations. Physical Review Letters. DOI: 10.1103/PhysRevLett.103.150502

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