Abstract:

The Quantum Machine Learning is at the combination of the two of the most sought study topics today, i.e., quantum computing and conventional machine learning. Quantum machine learning is the hybrid of two fields. When it comes to quantum machine learning, the goal is to understand how findings from the quantum realm may be applied to challenges in machine learning. Quantity of data is required to properly train a traditional computation model is increasing all the time and is approaching the boundaries of what can be handled by conventional computing machines. The use of quantum computing may help in the continuation of training with large amounts of data in such a circumstance. Quantum machine learning aims to create learning algorithms at a quicker rate than their conventional equivalents, according to the researchers. Classical machine learning is concerned with the search for data patterns and the use of data patterns to predict future occurrences in the data. However, quantum systems exhibit abnormal patterns that are not reproducible by classical systems, leading some to speculate that quantum computers may be able to outperform conventional computers on machine learning tasks in the future. The prior literature on quantum machine learning is reviewed in this paper, as well as the present state of the field.

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