09/10/2015, 16:30 — 17:30 — Seminar room (2.8.3), Physics Building
Masoud Mohseni, Google Quantum Artificial Intelligence Lab
Quantum Machine Learning
Over the past 30 years, two different computational paradigms have been developed based on the premise that the laws of quantum mechanics could provide radically new and more powerful methods of information processing. One of these approaches is to encode the solution of a computational problem into the ground state of a programmable many-body quantum Hamiltonian system. Although, there is empirical evidence for quantum enhancement in certain problem instances, there is not a full theoretical understanding of the conditions for quantum speed up for problems of practical interest, especially hard combinatorial optimization and inference tasks in machine learning. In his talk, I will provide an overview of quantum computing paradigms and discuss the progress at the Google Quantum Artificial Intelligence Lab towards developing the general theory and overcoming practical limitations. Furthermore, I will briefly discuss two recent quantum machine learning primitives that we have developed known as Quantum Principal Component Analysis and Multiqubit Quantum Tunneling.