PIRSA:20100025

Video URL

https://pirsa.org/20100025

Controlling Majorana zero modes with machine learning

Coopmans, L. (2020). Controlling Majorana zero modes with machine learning. Perimeter Institute for Theoretical Physics. https://pirsa.org/20100025

Coopmans, Luuk. Controlling Majorana zero modes with machine learning. Perimeter Institute for Theoretical Physics, Oct. 02, 2020, https://pirsa.org/20100025 

          @misc{ scivideos_PIRSA:20100025,
            doi = {10.48660/20100025},
            url = {https://pirsa.org/20100025},
            author = {Coopmans, Luuk},
            keywords = {Quantum Matter},
            language = {en},
            title = {Controlling Majorana zero modes with machine learning},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2020},
            month = {oct},
            note = {PIRSA:20100025 see, \url{https://scivideos.org/pirsa/20100025}}
          }

Luuk Coopmans Dublin Institute for Advanced Studies

Talk numberPIRSA:20100025
DOI10.48660/20100025
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

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 one-dimensional 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 counter-intuitive transport strategy. This strategy, which we dubbed jump-move-jump, 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 many-body dynamical systems with performance levels that make it relevant to the realization of large-scale quantum technology.