How can we do Machine Learning on Noisy Intermediate Scale Quantum computers: case studies

Quantum Machine Learning is a promising avenue for the next generation Artificial Intelligence. Several quantum computing algorithms such as Deutsch's, Shor's and Grover's algorithms proved that quantum parallelism can be exploited to generate quantum advantage. If similar results hold for Quantum Machine Learning is still an open question.

I am going to make a quick introduction to Quantum computing explaining textbook algorithms.

Then discuss Machine Learning models that we are implementing on cloud based Noisy Intermediate-Scale Quantum computers (NISQ) with the the goal of finding such an advantage.

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