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The generation of images is an area of machine learning that has already made incredible strides with classical algorithms and is expected to grow further with quantum resources. There are new reports of excellent success in developing a hybrid technique that will undoubtedly accelerate machine learning at quantum speed, sooner, not later, using a hybrid quantum-classical algorithm.
The new algorithm improves over previous classical methods for learning the MNIST dataset. The technique was used to generate high-quality handwritten digits on an ion trapping gate-based quantum platform from an ionq quantum computing company. The ionq system runs based on Ion trapping technology.
A groundbreaking step forward in the development of quantum machine learning.
Resources:
Machine Learning sample MNIST database
THE MNIST DATABASE
of handwritten digits has a training set of 60,000 examples, and a test set of 10,000 examples.
Method used:
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