Simulating alternating bias assisted annealing of amorphous oxide tunnel junctions

TL;DR

This paper explores how alternating bias assisted annealing (ABAA) reduces defects in amorphous oxide tunnel barriers for superconducting qubits by using simulations to understand its effects on the energy landscape.

Contribution

It introduces a simulation-based approach combining ab-initio molecular dynamics and machine learning to analyze ABAA's impact on barrier defects.

Findings

ABAA reduces defect density in amorphous oxide barriers.

Simulations reveal changes in the energy landscape due to ABAA.

Insights into defect mitigation mechanisms for qubit stability.

Abstract

Amorphous oxide tunneling barriers, primarily formed from aluminum, represent one of the most widely adopted platforms for superconducting quantum bits (qubits). To overcome challenges associated with defects and sample variance among the tunneling barriers, the methodology of alternating bias assisted annealing (ABAA) was introduced in Pappas et. al[1]. The process of applying alternating bias to the barrier and subsequently aging before use was shown to reduce defects in the barrier. Namely, defects that give rise to two-level systems, coupling to the qubit and expediting decoherence. In this work we replicate an expedited ABAA process through a combination of ab-initio molecular dynamics and machine-learned potentials, illuminating how ABAA effects the energy landscape of the barrier.