Quantum Machine Learning: The Convergence of AI and Quantum Computing for Next-Generation Algorithms
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V5I2P103Keywords:
Quantum Machine Learning, Quantum Computing, Machine Learning, Quantum Algorithms, Variational Quantum Algorithms, Quantum Neural Networks, Quantum Support Vector Machines, Quantum Generative Adversarial NetworksAbstract
The burgeoning field of Quantum Machine Learning (QML) represents a transformative convergence of artificial intelligence and quantum computing. Harnessing the unique capabilities of quantum mechanics, QML aims to develop novel algorithms that outperform classical machine learning approaches in specific tasks, potentially revolutionizing fields such as drug discovery, materials science, finance, and cybersecurity. This paper explores the fundamental principles of QML, examines key algorithms and architectures like Quantum Support Vector Machines (QSVMs), Variational Quantum Eigensolver (VQE) for Machine Learning, and Quantum Generative Adversarial Networks (QGANs), and discusses the current challenges and future opportunities in realizing the full potential of this exciting interdisciplinary field. We also critically analyze the potential of QML to address computationally intractable problems and provide a roadmap for future research directions
References
[1] DataScientest. Distributed architecture: Definition and relationship to big data. https://datascientest.com/en/distributed-architecture-definition-and-relationship-to-big-data
[2] Emeritus. How to use distributed computing? https://emeritus.org/blog/how-to-use-distributed-computing/
[3] International Journal of Engineering and Computer Science. Exploring distributed computing in modern applications. https://www.ijecs.in/index.php/ijecs/article/view/4954
[4] LinkedIn. What are the benefits of distributed computing in big data processing? 2025, from https://www.linkedin.com/advice/0/what-benefits-distributed-computing-big-data-processing
[5] MDPI. (2024). Advancements in distributed computing for big data applications. Applied Sciences, 14(1), 452. https://www.mdpi.com/2076-3417/14/1/452
[6] National Institutes of Health. (2015). Scalable distributed computing frameworks for big data analytics. Journal of Big Data, 2(1). https://pmc.ncbi.nlm.nih.gov/articles/PMC4505391/
[7] ResearchGate. (2024). Big data meets AI: Optimizing distributed computing for scalable machine learning models. https://www.researchgate.net/publication/388526469_Big_Data_Meets_AI_Optimizing_Distributed_Computing_for_Scalable_Machine_Learning_Models
[8] ResearchGate. (2017). Distributed computing in big data analytics: Concepts, technologies, and applications. https://www.researchgate.net/publication/317427131_Distributed_Computing_in_Big_Data_Analytics_Concepts_Technologies_and_Applications
