Knocking on Turing's door: Quantum Computing and Machine Learning

We have all grown accustomed to seeing and using a contemporary computer. Each year, industry behemoths like Intel, AMD, ARM, and NVIDIA, release the next generation of their top-of-the-line silicon, locking horns, and pushing the envelope of the traditional computers that we know today. If we critically evaluate these multitudes of new multi-core CPUs, GPUs, and mammoth compute clusters hosted on the cloud, we will soon realize that faster processors do not necessarily result in increased computational power. Granted, the speed of computation has increased exponentially in the past decades, so has the amount of data we can handle and process. We can store and analyze exabytes of data on the internet, train deep learning models like OpenAI's GPT-3, and enable the computational intelligence needed to defeat champions and grandmasters at complex games like Go and Chess. But have all these technological advances expanded what we can fundamentally do with computers beyond where we started out with? Or simply put, have we changed our traditional model of computing? Modern computers operate according to the principle of a von Neumann architecture (Ogban et.al, 2007).