perspectiva aiqbenchMachine learning

This study leverages machine learning and density functional theory to model how interstitial hydrogen creates two-level systems that cause microwave losses in niobium and tantalum oxides. By directly linking atomic-scale impurity landscapes to macroscopic dielectric loss, the work provides a predictive framework for mitigating decoherence in superconducting quantum circuits.

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From Atomistic Impurity Landscapes to Two-Level-System Distributions and Microwave Loss: Hydrogen-Induced Losses in Nb$_2$O$_{5-\delta}$ and Ta$_2$O$_{5-\delta}$

arXiv19 octombrie 20261 min de cititArticolul și perspectiva sunt în engleză.

arXiv:2603.17194v2 Announce Type: replace-cross Abstract: We introduce a first-principles-guided framework for predicting statistical TLS distributions from mobile impurities in disordered materials. Machine-learned interatomic potentials, with DFT and minimum-action-path validation, connect impurity landscapes to tunnel-splitting and asymmetry distributions, TLS densities, and dielectric loss. Applied to amorphous Nb$_2$O$_{5-\delta}$ and Ta$_2$O$_{5-\delta}$, the framework implicates interstitial hydrogen as a source of microwave loss. Loss tangents match Nb-pentoxide measurements at inferred H concentrations, while weaker H uptake in Ta reproduces the Nb/Ta TLS contrast.

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