Gabriele Di Gianluca | Searcharxiv

arXiv2026

Krypton-sputtered tantalum films for scalable high-performance quantum devices

Superconducting qubits based on tantalum (Ta) thin films have demonstrated the highest-performing microwave resonators and qubits. This makes Ta an attractive material for superconducting quantum computing applications, but, so far, direct deposition has largely relied on high substrate temperatures exceeding \SI{400}{\celsius} to achieve the body-centered cubic phase, BCC (\textalpha-Ta). This leads to compatibility issues for scalable fabrication leveraging standard semiconductor fabrication lines. Here, we show that changing the sputter gas from argon (Ar) to krypton (Kr) promotes BCC Ta synthesis on silicon (Si) at temperatures as low as \SI{200}{\celsius}, providing a wide process window compatible with back-end-of-the-line fabrication standards. Furthermore, we find these films to have substantially higher electronic conductivity, consistent with clean-limit superconductivity. We validated the microwave performance through coplanar waveguide resonator measurements, finding that films deposited at \SI{250}{\celsius} and \SI{350}{\celsius} exhibit a tight performance distribution at the state of the art. Higher temperature-grown films exhibit higher losses, in correlation with the degree of Ta/Si intermixing revealed by cross-sectional transmission electron microscopy. Finally, with these films, we demonstrate transmon qubits with a relatively compact, \SI{20}{\micro\meter} capacitor gap, achieving a median quality factor up to 14 million.

quant-ph↗

arXiv2025

Guided Diffusion for the Discovery of New Superconductors

The inverse design of materials with specific desired properties, such as high-temperature superconductivity, represents a formidable challenge in materials science due to the vastness of chemical and structural space. We present a guided diffusion framework to accelerate the discovery of novel superconductors. A DiffCSP foundation model is pretrained on the Alexandria Database and fine-tuned on 7,183 superconductors with first principles derived labels. Employing classifier-free guidance, we sample 200,000 structures, which lead to 34,027 unique candidates. A multistage screening process that combines machine learning and density functional theory (DFT) calculations to assess stability and electronic properties, identifies 773 candidates with DFT-calculated $T_\mathrm{c}>5$ K. Notably, our generative model demonstrates effective property-driven design. Our computational findings were validated against experimental synthesis and characterization performed as part of this work, which highlighted challenges in sparsely charted chemistries. This end-to-end workflow accelerates superconductor discovery while underscoring the challenge of predicting and synthesizing experimentally realizable materials.

cond-mat.supr-con↗