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When I heard of quantum computing for the first time, I think it was around 2008, researchers had successfully entangled qubits and were able to control them. Then, of course, Star Trek-like transportation came to mind when I heard two physically apart particles could share a state so that it was possible to change the state of one particle by observing the other. Yet, until around 2014, I did not pay much attention. I was too busy writing my doctoral dissertation about assessing the effort caused by the requirements in a software development project. When I returned to everyday life, I was just right in time to experience the end of the second AI winter and the advent of practical machine learning. What had been theory thus far became a reality now. But, soon, I recognized that the models we’re developing today have become increasingly hard to train. For instance, Open AI’s GPT-3 model that uses deep learning to produce human-like text would require 355 years on a single GPU. Thus, it is hard to believe that we can reach the upcoming milestones classically. This insight brought quantum computing back into my focus. Quantum computing promises to reduce the computational compl... See More