# Controlling Majorana zero modes with machine learning

### APA

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

### MLA

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

### BibTex

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

Luuk Coopmans Dublin Institute for Advanced Studies

## 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.