Machine-learned tuning to protected states by probing noise resilience

TL;DR

This paper introduces a machine-learning approach that injects noise to efficiently locate and tune quantum systems into protected states with high noise resilience, aiding quantum technology development.

Contribution

The authors develop a novel machine-learning method using noise injection and evolutionary strategies to find protected quantum states, including Majorana bound states.

Findings

Successfully tuned Kitaev chains to protected regimes with Majorana states

Method remains effective under various physical perturbations

Provides a reliable way to access noise-resilient quantum states

Abstract

Protected states are promising for quantum technologies due to their intrinsic resilience against noise. However, such states often emerge at discrete points or small regions in parameter space and are thus difficult to find in experiments. In this work, we present a machine-learning method for tuning to protected regimes, based on injecting noise into the system and searching directly for the most noise-resilient configuration. We illustrate this method by considering short quantum dot-based Kitaev chains which we subject to random parameter fluctuations. Using the covariance matrix adaptation evolutionary strategy we minimize the typical resulting ground state splitting, which makes the system converge to a protected configuration with well-separated Majorana bound states. We verify the robustness of our method by considering finite Zeeman fields, electron-electron repulsion, asymmetric couplings, and varying the length of the Kitaev chain. Our work provides a reliable method for tuning to protected states, including but not limited to isolated Majorana bound states.