A Detailed Overview of Quantum Computing Machine Learning Techniques

The computing sector has undergone radical changes in the last few decades. Conventional computers use binary (1s and 0s) numbers to perform tasks that are specified by the user. Using the concepts of quantum physics, a unique method known as "quantum computing" addresses problems that are...

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Bibliographic Details
Published in2024 International Conference on Communication, Computer Sciences and Engineering (IC3SE) pp. 1721 - 1725
Main Authors Aggarwal, Ada, Singh, S Vikram, Bansal, Saloni, Bhutani, Vijay
Format Conference Proceeding
LanguageEnglish
Published IEEE 09.05.2024
Subjects
Computers
Machine learning
Performance evaluation
Quantum algorithm
Quantum computing
Quantum state
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Summary:The computing sector has undergone radical changes in the last few decades. Conventional computers use binary (1s and 0s) numbers to perform tasks that are specified by the user. Using the concepts of quantum physics, a unique method known as "quantum computing" addresses problems that are too complicated for conventional computing devices. Two of the scientific fields with the fastest growth rates in today's time are machine learning and quantum computing. Researchers have recently initiated investigations into the ways in which conventional machine learning methods can be improved by quantum computing. Quantum machine learning comprises hybrid approaches that combine quantum and classical algorithms, using quantum methods to evaluate quantum states rather than traditional data. Current data science techniques could be significantly improved by quantum algorithms. In this work, we summarize the contributions made by different researchers in the domain of Quantum Learning and then look at some of the methods related to its real-world uses.
DOI:10.1109/IC3SE62002.2024.10593221