Join us to learn from one of the best in the field, globally recognized pioneer of quantum machine learning, Dr. Roger Melko. The power of quantum computers comes from the collective behavior of infinitely complex assemblies of interacting qubits. While giving quantum computers their power, manipulating this complexity also presents significant challenges for scientists and engineers building the current generation of quantum devices. Recently, it was demonstrated that industry-standard generative models could be adapted to learn powerful representations of quantum computers when trained on data produced by measuring qubit states. In this talk, I will explain how such machine learning techniques can be used to represent quantum wavefunctions on a conventional computer efficiently and to generate measurement probes inaccessible to conventional laboratory techniques. I demonstrate the power of this approach with real experimental qubit data obtained from a quantum computer composed of individual atoms, cooled to a fraction of a degree above absolute zero. Such generative models, together with state-of-the-art representations, will open the door for the machine learning assisted design of quantum computers well into the future.
.Quantum computers .Quantum friendly machine-learning techniques .Future of machine learning in the age of quantum computing machines