Nathan Wiebe’s research is currently focused on designing quantum algorithms for simulating physical systems and for machine learning as well as developing machine learning methods for characterizing quantum systems. His work in simulation includes the development of the first near-optimal methods for simulating time-dependent quantum systems, the invention of linear-combinations of unitaries simulation methods, plane-wave based simulation methods and a host of improvements to quantum chemistry simulation that have reduced the costs of such quantum computations by many orders of magnitude. In quantum machine learning, he has provided the first algorithms for deep learning on quantum computers, developed faster quantum methods for training perceptrons, clustering, gradient descent, least squares fitting and boosting as well as inventing the field of quantum Hamiltonian learning. Finally, his work also has led to the development of particle filter methods for inferring models for physical systems that are often more efficient and robust than existing methods used in theory or experiment. He has also done extensive work studying the adiabatic theorem and quantum chaos.
Nathan Wiebe received his PhD in Physics from the university of Calgary in 2011 in quantum computing. He then received a postdoctoral fellowship from the institute for quantum computing at the university of Waterloo before joining Microsoft Research’s quantum architecture and computing group in 2013.
CV can be found here.