Controlling Majorana zero modes with machine learning Speaker(s): Luuk Coopmans
Abstract:
Majorana zero modes have attracted much interest in recent years because of their promising properties for topological quantum computation. A key question in this regard is how fast two Majoranas can be exchanged giving rise to a unitary gate operation. In this presentation I will first explain that the transport of Majoranas in onedimensional topological superconductors can be formulated as a “simple” optimal control optimization problem for which we propose several different control regimes. Next I will discuss the optimization methods, Differential Programming and Natural Evolution Strategies, that were applied to the Majorana control problem and came up with a counterintuitive transport strategy. This strategy, which we dubbed jumpmovejump, will form the focus of the last part of the presentation in which I explain the key underlying mechanisms behind the strategy by reformulating the motion of Majoranas in a moving frame. I will conclude by arguing that these results demonstrate that machine learning for quantum control can be applied efficiently to quantum manybody dynamical systems with performance levels that make it relevant to the realization of largescale quantum technology. Date: 02/10/2020  11:00 am
Series: Machine Learning Initiative
