arXiv subjects

Elizabeth Hedrick

Publications and source records attributed to Elizabeth Hedrick.

6 recordsLinked to original sources

arXiv2026

Beta Tantalum Transmon Qubits with Quality Factors Approaching 10 Million

Tantalum-based transmon qubits are a promising platform for building large-scale quantum processors. So far, these qubits have been made from tantalum films grown exclusively in the alpha phase ({\alpha}-Ta). The beta phase of tantalum (\{beta}-Ta) readily nucleates at room temperature, making it attractive for scalable qubit fabrication. However, \{beta}-Ta is widely believed to be detrimental to qubit performance because it has a lower superconducting critical temperature than {\alpha}-Ta. We challenge this prevailing belief by fabricating low-loss transmon qubits from \{beta}-Ta films on sapphire. Across 11 qubits, the mean time-averaged quality factor is (5.6 +/- 2.3) x 10^6, with the best qubit recording a time-averaged quality factor of (10.1 +/- 1.3) x 10^6. Resonator studies demonstrate that the dominant microwave loss channel is surface two-level systems, with the surface loss contribution for \{beta}-Ta being about twice that of {\alpha}-Ta. \{beta}-Ta films exhibit significant kinetic inductance, consistent with an estimated magnetic penetration depth of (1.78 +/- 0.02) {\mu}m. This work establishes \{beta}-Ta on sapphire as a material platform for realizing low-loss transmon qubits and other superconducting devices such as compact resonators, kinetic inductance detectors, and quasiparticle traps.

quant-ph↗

arXiv2026

Quantifying surface losses in superconducting aluminum microwave resonators

The recent realization of millisecond-scale coherence with tantalum-on-silicon transmon qubits showed that depositing the Al/AlOx/Al Josephson junction in a high purity, ultrahigh vacuum environment was critical for achieving lifetime-limited coherence, motivating careful examination of the aluminum surface two-level system (TLS) bath. Here, we measure the microwave absorption arising from surface TLSs in superconducting aluminum resonators, following methodology developed for tantalum resonators. We vary film and surface properties and correlate microwave measurements with materials characterization. We find that the lifetimes of superconducting aluminum resonators are primarily limited by surface losses associated with TLSs in the 2.7 nm-thick native AlOx. Treatment with 49% HF removes surface AlOx completely; however, rapid oxide regrowth limits improvements in surface loss and long term device stability. Using these measurements we estimate that TLSs in aluminum interfaces contribute around 27% of the relaxation rate of state-of-the-art tantalum-on-silicon qubits that incorporate aluminum-based Josephson junctions.

quant-ph↗

arXiv2025

2D transmons with lifetimes and coherence times exceeding 1 millisecond

Materials improvements are a powerful approach to reducing loss and decoherence in superconducting qubits because such improvements can be readily translated to large scale processors. Recent work improved transmon coherence by utilizing tantalum (Ta) as a base layer and sapphire as a substrate. The losses in these devices are dominated by two-level systems (TLSs) with comparable contributions from both the surface and bulk dielectrics, indicating that both must be tackled to achieve major improvements in the state of the art. Here we show that replacing the substrate with high-resistivity silicon (Si) dramatically decreases the bulk substrate loss, enabling 2D transmons with time-averaged quality factors (Q) exceeding 1.5 x 10^7, reaching a maximum Q of 2.5 x 10^7, corresponding to a lifetime (T_1) of up to 1.68 ms. This low loss allows us to observe decoherence effects related to the Josephson junction, and we use improved, low-contamination junction deposition to achieve Hahn echo coherence times (T_2E) exceeding T_1. We achieve these material improvements without any modifications to the qubit architecture, allowing us to readily incorporate standard quantum control gates. We demonstrate single qubit gates with 99.994% fidelity. The Ta-on-Si platform comprises a simple material stack that can potentially be fabricated at wafer scale, and therefore can be readily translated to large-scale quantum processors.

quant-ph↗

arXiv2025

Vortex Motion Induced Losses in Tantalum Resonators

Tantalum (Ta) based superconducting circuits have been demonstrated to enable record qubit coherence times and quality factors, motivating a careful study of the microscopic origin of the remaining losses that limit their performance. We have recently shown that the losses in Ta-based resonators are dominated by two-level systems (TLSs) at low microwave powers and millikelvin temperatures. We also observe that some devices exhibit loss that is exponentially activated at a lower temperature inconsistent with the superconducting critical temperature (Tc) of the constituent film. Specifically, dc resistivity measurements show a Tc of over 4 K, while microwave measurements of resonators fabricated from these films show losses that increase exponentially with temperature with an activation energy as low as 0.3 K. Here, we present a comparative study of the structural and thermodynamic properties of Ta-based resonators and identify vortex motion-induced loss as the source of thermally activated microwave loss. Through careful magnetoresistance and x-ray diffraction measurements, we observe that the increased loss occurs for films that are in the clean limit, where the superconducting coherence length is shorter than the mean free path. Vortex motion-induced losses are suppressed for films in the dirty limit, which show evidence of structural defects that can pin vortices. We verify this hypothesis by explicitly pinning vortices via patterning and find that we can suppress the loss by microfabrication.

cond-mat.supr-con↗

arXiv2023

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.

cond-mat.supr-con↗

arXiv2022

Closed-loop machine learning for discovery of novel superconductors

The discovery of novel materials drives industrial innovation, although the pace of discovery tends to be slow due to the infrequency of "Eureka!" moments. These moments are typically tangential to the original target of the experimental work: "accidental discoveries". Here we demonstrate the acceleration of intentional materials discovery - targeting material properties of interest while generalizing the search to a large materials space with machine learning (ML) methods. We demonstrate a closed-loop ML discovery process targeting novel superconducting materials, which have industrial applications ranging from quantum computing to sensors to power delivery. By closing the loop, i.e. by experimentally testing the results of the ML-generated superconductivity predictions and feeding data back into the ML model to refine, we demonstrate that success rates for superconductor discovery can be more than doubled. In four closed-loop cycles, we discovered a new superconductor in the Zr-In-Ni system, re-discovered five superconductors unknown in the training datasets, and identified two additional phase diagrams of interest for new superconducting materials. Our work demonstrates the critical role experimental feedback provides in ML-driven discovery, and provides definite evidence that such technologies can accelerate discovery even in the absence of knowledge of the underlying physics.

cond-mat.supr-con↗