Machine-guided Design of Oxidation Resistant Superconductors for Quantum Information Applications

Decoherence in superconducting qubits has long been attributed to two level systems arising from the surfaces and interfaces present in real devices. A recent significant step in reducing decoherence was the replacement of superconducting niobium by superconducting tantalum, resulting in a tripling of transmon qubit lifetimes (T1). One of these surface variables, the identity, thickness, and quality of the native surface oxide, is thought to play a major role as tantalum only has one oxide whereas niobium has several. Here we report the development of a thermodynamic metric to rank materials based on their potential to form a well-defined, thin, surface oxide. We first compute this metric for known binary and ternary metal alloys using data available from Materials Project, and experimentally validate the strengths and limits of this metric through preparation and controlled oxidation of 8 known metal alloys. Then we train a convolutional neural network to predict the value of this metric from atomic composition and atomic properties. This allows us to compute the metric for materials that are not present in materials project, including a large selection of known superconductors, and, when combined with Tc, allow us to identify new candidate superconductors for quantum information science (QISE) applications. We test the oxidation resistance of a pair of these predictions experimentally. Our results are expected to lay the foundation for tailored and rapid selection of improved superconductors for QISE.