Quantum Machine Learning: Exploring the Intersection of Quantum Computing and AI

Authors: Gaurav Kashyap

DOI: https://doi.org/10.5281/zenodo.14615549

Short DOI: https://doi.org/g8x3qk

Country: USA

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Abstract: At the nexus of artificial intelligence (AI) and quantum computing lies the emerging field of quantum machine learning (QML). By speeding up the computation of intricate algorithms, quantum computers have the potential to transform a number of fields, including machine learning, by outperforming classical computers by an exponential amount in specific tasks. This essay examines the fundamental ideas of quantum computing, how it applies to machine learning, and the potential advantages and difficulties of QML. We examine several quantum algorithms, including quantum versions of support vector machines, clustering, and neural networks, that can improve machine learning models. We also go over QML's drawbacks, present research directions, and potential future developments, providing insights into how quantum technologies might transform AI in the ensuing decades.
With the potential to outperform traditional supercomputers in resolving important issues in a variety of fields, including machine learning, quantum computing has become a ground-breaking technology. This study investigates the fascinating nexus between artificial intelligence and quantum computing, looking at how quantum machine learning might revolutionize classification, pattern recognition, and data processing.

Keywords: Quantum Computing, Quantum Machine Learning, Artificial Intelligence, Data Science, Healthcare

Paper Id: 232005

Published On: 2025-01-08

Published In: Volume 13, Issue 1, January-February 2025

Cite This: Quantum Machine Learning: Exploring the Intersection of Quantum Computing and AI - Gaurav Kashyap - IJIRMPS Volume 13, Issue 1, January-February 2025. DOI 10.5281/zenodo.14615549

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