In Quantum computing, users can physically control parameters like Electromagnetic field’s strength, frequency of a laser pulse, or others to solve problems. Thus, Quantum computers can be trained like neural networks. The biggest advantage of quantum computers is that they can produce patterns that classical systems are thought to have difficulties in producing. Therefore, it’s reasonable to assume that quantum computers may outperform classical computers on Machine Learning tasks. This has led to a new field called quantum machine learning.

Quantum technologies can enhance learning algorithms. This is known as quantum-enhanced machine learning. The most common application of quantum computers in the field refers to machine learning algorithms for the analysis of data that couldn’t be executed through classical computing.

Quantum Machine Learning increases the computation speed and can manage data storage done by algorithms in a programme. It extends the proof of learning by running machine learning algorithms on new computing devices- quantum computers. The information processing depends on quantum physics and its law, substantially different from computer models.


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