Characterization of loss mechanisms in a fluxonium qubit

Using a fluxonium qubit with in situ tunability of its Josephson energy, we characterize its energy relaxation at different flux biases as well as different Josephson energy values. The relaxation rate at qubit energy values, ranging more than one order of magnitude around the thermal energy $k_B T$, can be quantitatively explained by a combination of dielectric loss and $1/f$ flux noise with a crossover point. The amplitude of the $1/f$ flux noise is consistent with that extracted from the qubit dephasing measurements at the flux sensitive points. In the dielectric loss dominant regime, the loss is consistent with that arises from the electric dipole interaction with two-level-system (TLS) defects. In particular, as increasing Josephson energy thus decreasing qubit frequency at the flux insensitive spot, we find that the qubit exhibits increasingly weaker coupling to TLS defects thus desirable for high-fidelity quantum operations

Quantum Instruction Set Design for Performance

A quantum instruction set is where quantum hardware and software meet. We develop new characterization and compilation techniques for non-Clifford gates to accurately evaluate different quantum instruction set designs. We specifically apply them to our fluxonium processor that supports mainstream instruction $\mathrm{iSWAP}$ by calibrating and characterizing its square root $\mathrm{SQiSW}$. We measure a gate fidelity of up to $99.72\%$ with an average of $99.31\%$ and realize Haar random two-qubit gates using $\mathrm{SQiSW}$ with an average fidelity of $96.38\%$. This is an average error reduction of $41\%$ for the former and a $50\%$ reduction for the latter compared to using $\mathrm{iSWAP}$ on the same processor. This shows designing the quantum instruction set consisting of $\mathrm{SQiSW}$ and single-qubit gates on such platforms leads to a performance boost at almost no cost.Comment: 2 figures in main text and 21 figures in Supplementary Materials. This manuscript subsumes version 1 with significant improvements such as experimental demonstration and materials presentatio

Titanium Nitride Film on Sapphire Substrate with Low Dielectric Loss for Superconducting Qubits

Dielectric loss is one of the major decoherence sources of superconducting qubits. Contemporary high-coherence superconducting qubits are formed by material systems mostly consisting of superconducting films on substrate with low dielectric loss, where the loss mainly originates from the surfaces and interfaces. Among the multiple candidates for material systems, a combination of titanium nitride (TiN) film and sapphire substrate has good potential because of its chemical stability against oxidization, and high quality at interfaces. In this work, we report a TiN film deposited onto sapphire substrate achieving low dielectric loss at the material interface. Through the systematic characterizations of a series of transmon qubits fabricated with identical batches of TiN base layers, but different geometries of qubit shunting capacitors with various participation ratios of the material interface, we quantitatively extract the loss tangent value at the substrate-metal interface smaller than $8.9 \times 10^{-4}$ in 1-nm disordered layer. By optimizing the interface participation ratio of the transmon qubit, we reproducibly achieve qubit lifetimes of up to 300 $\mu$s and quality factors approaching 8 million. We demonstrate that TiN film on sapphire substrate is an ideal material system for high-coherence superconducting qubits. Our analyses further suggest that the interface dielectric loss around the Josephson junction part of the circuit could be the dominant limitation of lifetimes for state-of-the-art transmon qubits

The International Conference on Intelligent Biology and Medicine 2018: Medical Informatics Thematic Track (MedicalInfo2018)

Abstract In this editorial, we first summarize the 2018 International Conference on Intelligent Biology and Medicine (ICIBM 2018) that was held on June 10–12, 2018 in Los Angeles, California, USA, and then briefly introduce the six research articles included in this supplement issue. At ICIBM 2018, a special theme of Medical Informatics was dedicated to recent advances of data science in the medical domain. After peer review, six articles were selected in this thematic issue, covering topics such as clinical predictive modeling, clinical natural language processing (NLP), electroencephalogram (EEG) network analysis, and text mining in biomedical literature

Seasonal Diet Composition of Goitered Gazelle (<i>Gazella subgutturosa</i>) in an Arid and Semi-Arid Region of Western China

Global climate change, habitat fragmentation, and human interference have resulted in a significant, ongoing decline in the population of goitered gazelles. Effective conservation strategies require an understanding of resource requirements of threatened species, such as dietary needs. Therefore, we aimed to elucidate the food composition and seasonal dietary changes of goitered gazelles through microhistological analyses of fresh feces. Fabaceae (11.5%), Gramineae (9.4%), Chenopodiaceae (20.2%), Asteraceae (10.1%), and Rosaceae (19.5%) formed the primary dietary components of goitered gazelle. Additionally, Krascheninnikovia arborescens (13.4%) and Prunus sibirica (16.3%) were identified as the key forage plants. Forbs (50.4%) were the predominant plants for grazing throughout the year, particularly in the spring (72.9%). The proportion of trees in the diet was highest in the autumn (36.7%) and comparatively lower in other seasons. Furthermore, the proportions of shrubs (22.0%) and graminoids (14.8%) both reached their peaks in the winter. Our findings indicate that goitered gazelles strategically forage seasonally to cope with resource bottlenecks, enhancing their adaptability to arid and semi-arid habitats. Our study provides essential ecological information for the conservation of goitered gazelles and emphasizes the importance of dietary studies of species of ecological significance in environmentally sensitive areas