Investigating the Fusion of Quantum Computing in the Application of Machine Learning: A Research Exploration

DOI

10.2991/978-94-6463-948-3_7How to use a DOI?

Keywords

Quantum Computing; Machine Learning; Quantum Machine Learning; Quantum Algorithms; Variational Circuits; Quantum Data Encoding

Abstract

Quantum computing is a radical new computational paradigm based on the principles of quantum mechanics. Since it can utilize superposition, entanglement, and interference, it is considered as a potential solution to a variety of high-dimensional, computationally intensive problems that still cannot be solved by classical systems. ML, which is more and more dependent on HPC infrastructures, may gain a quantum computational model as a tremendous boon that can bring about quicker training, more expressive representations, and better optimization. This paper offers a thorough, academic, and conceptually demanding study of the QML fusion. After briefly summarizing the fundamental postulates of quantum mechanics, the paper reviews quantum data encoding strategies, quantum parallelism, continuous-variable representations, and Hamiltonian-based modeling. Some of the central quantum algorithms - such as Grover’s search, quantum linear-system solvers, quantum annealing, and qRAM - are introduced at a conceptual level, with only a few essential equations being kept. The article also discusses contemporary QML approaches such as quantum support vector machines, quantum neural networks, quantum clustering, and hybrid optimization methods. Issues like noise, decoherence, and qubit limitations are also dealt with critically. The goal is to describe the state of the art in QML, evaluate its current capabilities, and estimate possible future research and application directions.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Sanjesh Pawale
AU  - Kailas Patil
AU  - Ganesh Ingle
AU  - Sital Dash
PY  - 2026
DA  - 2026/01/06
TI  - Investigating the Fusion of Quantum Computing in the Application of Machine Learning: A Research Exploration
BT  - Proceedings of the International Conference on Sustainable Innovation with Artificial Intelligence and Machine Learning 2025 (ICSIAIML 2025)
PB  - Atlantis Press
SP  - 83
EP  - 98
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-948-3_7
DO  - 10.2991/978-94-6463-948-3_7
ID  - Pawale2026
ER  -