Quantum Machine Learning: Unraveling a New Paradigm in Computational
Intelligence
Abstract
Quantum Machine Learning (QML) is an advanced discipline that emerges
from the combined power of machine learning and quantum computing that
has the ability to address intricate challenges in several domains. The
domain of quantum machine learning investigates the development and
execution of quantum software with the potential to facilitate machine
learning at a much superior pace compared to traditional computers. This
research delves into the fundamental principles of quantum mechanics and
their crucial role in quantum computing, emphasizing the potential of
various quantum algorithms to surpass classical algorithms in specific
computational tasks, and then methodically navigates through the quantum
machine learning algorithms, offering profound insights into their
application potential in revolutionizing data analysis and complex
problem-solving methodologies, including their importance in the
Language Learning Models (LLM) and Language Analysis Models (LAM). The
study also provides insights into the various quantum platforms,
encompassing both hardware and software aspects for the implementation
of QML algorithms, and also explores the challenges prevalent in QML,
with a particular focus on the limitations imposed by existing quantum
hardware and the intricate nuances of data processing within quantum
frameworks. This study contributes by presenting the basis for future
research work related to the development of algorithms in the field of
quantum machine learning and anticipating the far-reaching impact of QML
across diverse scientific and technological domains.