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A Detailed Overview of Quantum Computing Machine Learning Techniques | IEEE Conference Publication | IEEE Xplore

A Detailed Overview of Quantum Computing Machine Learning Techniques


Abstract:

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 t...Show More

Abstract:

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.
Date of Conference: 09-11 May 2024
Date Added to IEEE Xplore: 23 July 2024
ISBN Information:
Conference Location: Gautam Buddha Nagar, India

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