Asymmetric-gate Mach--Zehnder interferometry in graphene: Multi-path conductance oscillations and visibility characteristics

TL;DR

This paper develops a phenomenological model for graphene-based Mach--Zehnder interferometers with asymmetric p--n junctions, demonstrating tunable interference patterns and improved analysis techniques using machine learning.

Contribution

It introduces a framework for asymmetric-gate graphene MZ interferometry, revealing multiple pathways and enhanced visibility under symmetric gating, with novel signal analysis methods.

Findings

Interferometer area is tunable by asymmetric gate potentials.

Multiple interference pathways emerge at higher filling factors.

Machine learning improves Fourier analysis of interference signals.

Abstract

Graphene provides an excellent platform for investigating electron quantum interference due to its outstanding coherent properties. In the quantum Hall regime, Mach--Zehnder (MZ) electronic interferometers are realized using p--n junctions in graphene, where electron interference is highly protected against decoherence. In this work, we present a phenomenological framework for graphene-based MZ interferometry with asymmetric p--n junction configurations. We show that the enclosed interferometer area can be tuned by asymmetric gate potentials, and additional MZ pathways emerge in higher-filling-factor scenarios, e.g. $\left(\nu_{n},\nu_{p}\right)=\left(-3,+3\right)$. The resulting complicated beat oscillations in asymmetric-gate MZ interference are efficiently analyzed using a machine-learning--based Fourier transform, which yields improved peak-to-background ratios compared to conventional signal-processing techniques. Furthermore, we examine the impact of the asymmetric gate on the interference visibility, finding that interference visibility is enhanced under symmetric gate conditions.