Optimizing Circuit Reusing and its Application in Randomized Benchmarking

Quantum learning tasks often leverage randomly sampled quantum circuits to characterize unknown systems. An efficient approach known as "circuit reusing," where each circuit is executed multiple times, reduces the cost compared to implementing new circuits. This work investigates the optimal reusing parameter that minimizes the variance of measurement outcomes for a given experimental cost. We establish a theoretical framework connecting the variance of experimental estimators with the reusing parameter R. An optimal R is derived when the implemented circuits and their noise characteristics are known. Additionally, we introduce a near-optimal reusing strategy that is applicable even without prior knowledge of circuits or noise, achieving variances close to the theoretical minimum. To validate our framework, we apply it to randomized benchmarking and analyze the optimal R for various typical noise channels. We further conduct experiments on a superconducting platform, revealing a non-linear relationship between R and the cost, contradicting previous assumptions in the literature. Our theoretical framework successfully incorporates this non-linearity and accurately predicts the experimentally observed optimal R. These findings underscore the broad applicability of our approach to experimental realizations of quantum learning protocols.Comment: 19 pages, 12 figures. Comments are welcomed

Optimizing Circuit Reusing and its Application in Randomized Benchmarking

Quantum learning tasks often leverage randomly sampled quantum circuits to characterize unknown systems. An efficient approach known as ``circuit reusing,'' where each circuit is executed multiple times, reduces the cost compared to implementing new circuits. This work investigates the optimal reusing times that minimizes the variance of measurement outcomes for a given experimental cost. We establish a theoretical framework connecting the variance of experimental estimators with the reusing times $R$. An optimal $R$ is derived when the implemented circuits and their noise characteristics are known. Additionally, we introduce a near-optimal reusing strategy that is applicable even without prior knowledge of circuits or noise, achieving variances close to the theoretical minimum. To validate our framework, we apply it to randomized benchmarking and analyze the optimal $R$ for various typical noise channels. We further conduct experiments on a superconducting platform, revealing a non-linear relationship between $R$ and the cost, contradicting previous assumptions in the literature. Our theoretical framework successfully incorporates this non-linearity and accurately predicts the experimentally observed optimal $R$. These findings underscore the broad applicability of our approach to experimental realizations of quantum learning protocols

Ligament and Droplet Generation by Oil Film on a Rotating Disk

The lubrication and heat transfer designs of bearing chamber depend on an understanding of oil/air two-phase flow. As initial and boundary conditions, the characteristics of ligament and droplet generation by oil film on rotating parts have significant influence on the feasibility of oil/air two-phase flow analysis. An integrated model to predict the oil film flow, ligament number, and droplet Sauter mean diameter (SMD) of a rotating disk, which is an abstraction of the droplet generation sources in a bearing chamber, is developed based on the oil film force balance analysis and wave theory. The oil film thickness and velocity, ligaments number, and droplet SMD are calculated as functions of the rotating disk radius, rotational speed and oil volume flow rate and oil properties. The theoretical results show that the oil film thickness and SMD are decreased with an increasing rotational speed, while the radial, transverse velocities, and ligament number are increased. The oil film thickness, radial velocity, and SMD are increased with an increasing oil flow rate, but the transverse velocity and ligament number are decreased. A test facility is built for the investigation into the ligament number of a rotating disk, and the measurement of ligament number is carried out by means of a high speed photography

Data to: Modeling long-term effect of dynamic vegetation on flood and landslide disasters at the catchment scale [Dataset]

<p>See the README file for the detailed description.</p&gt

Software to: Modeling long-term effect of dynamic vegetation on flood and landslide disasters at the catchment scale [Software]

<p>This model is an improved version of the open-source modeling framework iHydroSlide3D v1.0 (Chen et al., 2023; Chen et al., 2024), by integrating dynamic vegetation components as a submodule. Please download the compressed package and unzip it to get the code and manual.</p> <p> </p> <p>Reference:</p> <p>Chen, G., Zhang, K., Wang, S., Xia, Y., & Chao, L. (2023). iHydroSlide3D v1. 0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction. <em>Geoscientific Model Development, 16</em>(10), 2915-2937. <a href="https://doi.org/10.5194/gmd-16-2915-2023">https://doi.org/10.5194/gmd-16-2915-2023</a></p> <p>Chen G, Zhang K, Wang S, et al. PHyL v1. 0: A parallel, flexible, and advanced software for hydrological and slope stability modeling at a regional scale[J]. Environmental Modelling & Software, 2024, 172: 105882. <a href="https://doi.org/10.1016/j.envsoft.2023.105882">https://doi.org/10.1016/j.envsoft.2023.105882</a></p> <p> </p&gt

iHydroSlide3D_v1.0

iHydroSlide3D_v1.

iHydroSlide3D_v1.0

iHydroSlide3D_v1.0_releas