Machine learning in quantum experiments and technologies
Dr. Alexey Melnikov, University of Basel
Leuchs-Russell Auditorium, A.1.500, Staudtstr. 2
Today machine learning plays an increasing role in many aspects of scientific research. What can be the role of machine learning in quantum physics and, vice versa, the role of quantum physics in machine learning? In this talk both questions will be addressed by connecting quantum physics and machine learning in different ways.
I will talk about reinforcement learning agents and present the model of projective simulation, demonstrating how the deliberation of agents can be sped up via a quantum walk process. A future experiment implementing quantum-enhanced deliberation in a register of superconducting qubits will be discussed. It will be shown that the same reinforcement learning agent is now used to design new quantum optics experiments for three-photon entangled states generation, quantum states distribution in networks, and for violating CHSH inequalities. I will finish the talk by demonstrating how we improve our understanding of quantum walk advantages with convolutional neural networks.