It would be the harbinger of an entirely new medium of calculation, harnessing the powers of subatomic particles to obliterate the barriers of time in solving incalculable problems. You and I are being continually surveyed. We reveal information about ourselves with astonishingly little resistance. Social media has made many of us into veritable slot machines for our own personal data. We're fed a little token of encouragement that someone may yet like us, our arm is gently pulled, and we disgorge something we hope people will find valuable enough for commencing small talk. What personal facts, real or trivial, we do end up disclosing -- perhaps unwittingly -- immediately undergo unceasing analysis. The inferences these analyses draw about us as people are being aggregated, baselined, composited, deliberated, and profiled.
There is a strong hope (and hype) that Quantum Computers will help machine learning in many ways. Research in Quantum Machine Learning (QML) is a very active domain, and many small and noisy quantum computers are now available. Different approaches exist, for both long term and short term, and we may wonder what are their respective hopes and limitations, both in theory and in practice? It all started in 2009 with the publications of the "HHL" Algorithm  proving an exponential acceleration for matrix multiplication and inversion, which triggered exciting applications in all linear algebra-based science, hence machine learning. Since, many algorithms were proposed to speed up tasks such as classification , dimensionality reduction , clustering , recommendation system , neural networks , kernel methods , SVM , reinforcement learning , and more generally optimization .
It's not every day that an 18-year-old college student catches the eye of the computing world, but when Ewin Tang took aim at recommendation algorithms similar to those commonly used by the likes of Amazon and Netflix, the University of Texas at Austin mathematics and computer science undergraduate blew up an established belief: that classical computers cannot perform these types of calculations at the speed of quantum computers. In a July 2018 paper, which Tang wrote for a senior honors thesis under the supervision of computer science professor Scott Aaronson, a leading researcher in quantum computing algorithms, she discovered an algorithm that showed classical computers can indeed tackle predictive recommendations at a speed previously thought possible only with quantum computers. "I actually set out to demonstrate that quantum machine learning algorithms are faster," she explains. "But, along the way, I realized this was not the case." Ewin Tang set out to show that quantum machine learning algorithms are faster than classical algorithms, "but ... I realized this was not the case."
The third and final ICML2020 invited talk covered the topic of quantum machine learning (QML) and was given by Iordanis Kerenidis. He took us on a tour of the quantum world, detailing the tools needed for quantum machine learning, some of the first applications, and challenges faced by the field. Iordanis started his talk with a bit of background into quantum computing and why we should be interested in it. He stressed that we should not think of quantum computers as just being a faster processor and providing a blanket speed-up. Crucially, the quantum method is a fundamentally different way of performing computation; it could be much faster for certain tasks, but not all.
In the not-terribly-distant past, the goal of quantum computing research was to achieve a milestone called quantum supremacy: the point in time when a quantum computer can, in practical terms, be considered superior to a classical, semiconductor-based computer for processing any task you give it. Certainly Google already made a big enough fuss about it. This is no longer true. Engineers and scholars have since conceded that this is not possible -- that a quantum device cannot supersede a classical device. Quantum computers offer great promise for cryptography and optimization problems.