Simulating alternating bias assisted annealing of amorphous oxide tunnel junctions
Alexander C. Tyner, Alexander V. Balatsky
TL;DR
This work tackles TLS-induced decoherence in amorphous oxide barriers of superconducting qubits by computationally reproducing the alternating bias assisted annealing (ABAA) protocol using Car-Parrinello MD and machine-learned interatomic potentials. By generating an Al–a-Al2O3–Al barrier, applying $0.5$ V bias pulses at $30$ K with aging, and analyzing the energy landscape and vibrational modes via Phonopy and the MACE potential, the authors demonstrate that ABAA drives the system toward deeper energetic minima and reduces low-frequency soft modes. The results indicate a reduction of TLS-related vibrational density of states below $0.5$ THz, though TLSs are not eliminated and may be shifted to higher frequencies outside the qubit coupling window. These findings support ABAA as a viable strategy to enhance qubit coherence and offer computational guidance for optimizing bias strength, temperature, and pulse count.
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.
