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Research Paper | Computer Science & Engineering | India | Volume 11 Issue 3, March 2022 | Popularity: 4.9 / 10

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Unlocking the Power of Quantum Mechanics for Machine Learning

Brahmaleen Kaur Sidhu

Abstract: Machine learning has radically changed the way problems in various disciplines are solved but faces limitations in tackling increasingly complex and high-dimensional data. Quantum machine learning emerges as a burgeoning paradigm, harnessing the principles of quantum mechanics to potentially surpass classical approaches. This paper delves into the core concepts of quantum machine learning, exploring its algorithms and challenges. The incorporation of quantum mechanics in machine learning algorithms offers a more expressive way to represent data compared to classical methods by capturing intricate relationships within complex datasets. Superposition and entanglement empower the development of algorithms that can tackle computationally expensive tasks in classical machine learning, particularly in high-dimensional spaces. Limited qubit count and susceptibility to noise in current quantum computers hinder the practical implementation of quantum machine learning for large-scale problems. Designing and optimizing efficient quantum algorithms tailored to specific machine learning tasks remains an active area of research. The paper concludes by highlighting the immense potential of quantum machine learning to revolutionize various fields, from materials science and drug discovery to finance and artificial intelligence.

Keywords: artificial intelligence, machine learning, quantum computing, quantum machine learning, quantum speed up

Edition: Volume 11 Issue 3, March 2022

Pages: 1574 - 1582

DOI: https://www.doi.org/10.21275/SR24427201406

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