Through the integration of machine learning techniques with the
computational power of quantum computing, quantum algorithms greatly improve the
processing and analytical capabilities of systems. In contrast to traditional techniques,
quantum computing effectively manages large and intricate datasets, providing answers
to issues that conventional systems are unable to handle. Quantum Machine Learning
(QML) has shown promise in a variety of fields, such as large-scale optimization,
image recognition, and natural language processing. Because it can produce better and
faster results, QML is positioned to revolutionize industries that depend on insights
from data. The implementation of QML faces operational and scalability complications
when used in healthcare applications and other fields of practical use. This study
evaluates the active QML algorithms while demonstrating their improved functionality
compared to usual approaches, and it identifies implementation barriers in real-world
scenarios.
Keywords: AI ethicsAutomationComputer visionData encoding, Data privacy, Deep learning, Entanglement, Linear regression, ml, Pattern recognition, Predictive analysis, Quantum computing, Quantum data processing, Quantum neural networks, Quantum speedup, Superposition
