Unlocking the Potential of Quantum-Aided Machine Learning for Generative Tasks

Quantum-Aided Machine learning Shows its Value

A machine-learning algorithm incorporating a quantum circuit produces realistic handwritten numbers and is more efficient than its classic counterpart.

Machine learning allows computers recognize complex patterns, such as faces, and create new examples that look realistic. Researchers have demonstrated the first time that a quantum algorithm can generate realistic-looking examples. In this case, they created authentic-looking handwritten numbers [1]. Researchers see this as a major step towards building quantum devices that can go beyond classical machine learning.

The most common use of neural networks is classification–recognizing handwritten letters, for example. Researchers are increasingly using algorithms to perform more creative tasks, such as creating new, realistic artworks, music pieces, or human face. These so-called \”generative neural networks\” can be used to edit photos automatically, removing unwanted details such as rain.


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