From Theory to Practice: Quantum Machine Learning in Action

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Taghandiki Kazem

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Journal Paper

Paper Information

Journal: Journal of Engineering and Applied Research Year:2024 | Volume:1 | Issue:2 Page(s): 29-48

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Information Journal Paper

Title

From Theory to Practice: Quantum Machine Learning in Action

Author(s)

Taghandiki Kazem | Issue Writer Certificate

Keywords

Machine Learning

Quantum computing

superposition

Qubits

Quantum Algorithms

Abstract

This research on quantum Machine Learning (QML) represents the culmination of a thorough investigation spanning four months and drawing upon insights from more than 20 reputable scholarly articles. Quantum Machine Learning stands at the convergence of Quantum computing and Machine Learning, offering a unique avenue to revolutionize traditional algorithms by leveraging principles from quantum mechanics. This abstract delves into the foundational aspects of QML, elucidating key components such as Qubits, quantum gates, superposition, and entanglement. Additionally, it explores various QML algorithms, including quantum neural networks, quantum support vector machines, and quantum clustering, which exploit quantum properties to tackle intricate computational challenges. The abstract further discusses the wide spectrum of applications where QML holds promise, ranging from quantum chemistry and optimization problems to cryptography and big data analysis. Despite the considerable potential, QML confronts obstacles such as scalability issues, noise, and error correction. To surmount these hurdles and unlock the full potential of quantum Machine Learning, sustained research efforts and collaborative endeavours are imperative, poised to drive transformative advancements across diverse industries.

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