Quantum Machine Learning (QML) is emerging as an application for quantum computers with the potential to be the killer app. With the successful application of machine learning methods In industry applications and the emerging deep learning renaissance is it natural to extrapolate that quantum computers, once fully capable, will play a transformative role in the way we think of computation and...
Machine learning has the potential to be an important early application area for quantum computers. In this presentation I will discuss the Quantum Science Program at Fermilab at a high level, and then zoom in on a pair of recently awarded DOE grants that include machine learning applications as part of their portfolios. I will discuss the applications we're exploring and chart a course for...
Inherently quantum calculators may be able to empower quantum field theory calculations. After discussing existing results for scattering amplitudes and lattice methods, I will motivate a new direction where interference and entanglement effects are largely ignored in current high energy simulations.
We survey the field of quantum machine learning, in particular the algorithmic tools which are used in modern quantum ML algorithms. We review both heuristic approaches, such as quantum neural and tensor networks, as well as provable approaches based on the HHL algorithm and quantum linear algebra. We also discuss the varying applications, and amenability to near-term quantum computers.
