Symmetry-Based Quantum Circuit Mapping

Quantum circuit mapping is a crucial process in the quantum circuit compilation pipeline, facilitating the transformation of a logical quantum circuit into a list of instructions directly executable on a target quantum system. Recent research has introduced a post-compilation step known as remapping, which seeks to reconfigure the initial circuit mapping to mitigate quantum circuit errors arising from system variability. As quantum processors continue to scale in size, the efficiency of quantum circuit mapping and the overall compilation process has become of paramount importance. In this work, we introduce a quantum circuit remapping algorithm that leverages the intrinsic symmetries in quantum processors, making it well-suited for large-scale quantum systems. This algorithm identifies all topologically equivalent circuit mappings by constraining the search space using symmetries and accelerates the scoring of each mapping using vector computation. Notably, this symmetry-based circuit remapping algorithm exhibits linear scaling with the number of qubits in the target quantum hardware and is proven to be optimal in terms of its time complexity. Moreover, we conduct a comparative analysis against existing methods in the literature, demonstrating the superior performance of our symmetry-based method on state-of-the-art quantum hardware architectures and highlighting the practical utility of our algorithm, particularly for quantum processors with millions of qubits.Comment: 10 pages, 5 figures; comments are welcom

Bid Optimization by Multivariable Control in Display Advertising

Real-Time Bidding (RTB) is an important paradigm in display advertising, where advertisers utilize extended information and algorithms served by Demand Side Platforms (DSPs) to improve advertising performance. A common problem for DSPs is to help advertisers gain as much value as possible with budget constraints. However, advertisers would routinely add certain key performance indicator (KPI) constraints that the advertising campaign must meet due to practical reasons. In this paper, we study the common case where advertisers aim to maximize the quantity of conversions, and set cost-per-click (CPC) as a KPI constraint. We convert such a problem into a linear programming problem and leverage the primal-dual method to derive the optimal bidding strategy. To address the applicability issue, we propose a feedback control-based solution and devise the multivariable control system. The empirical study based on real-word data from Taobao.com verifies the effectiveness and superiority of our approach compared with the state of the art in the industry practices

Boosting Meta-Training with Base Class Information for Few-Shot Learning

Few-shot learning, a challenging task in machine learning, aims to learn a classifier adaptable to recognize new, unseen classes with limited labeled examples. Meta-learning has emerged as a prominent framework for few-shot learning. Its training framework is originally a task-level learning method, such as Model-Agnostic Meta-Learning (MAML) and Prototypical Networks. And a recently proposed training paradigm called Meta-Baseline, which consists of sequential pre-training and meta-training stages, gains state-of-the-art performance. However, as a non-end-to-end training method, indicating the meta-training stage can only begin after the completion of pre-training, Meta-Baseline suffers from higher training cost and suboptimal performance due to the inherent conflicts of the two training stages. To address these limitations, we propose an end-to-end training paradigm consisting of two alternative loops. In the outer loop, we calculate cross entropy loss on the entire training set while updating only the final linear layer. In the inner loop, we employ the original meta-learning training mode to calculate the loss and incorporate gradients from the outer loss to guide the parameter updates. This training paradigm not only converges quickly but also outperforms existing baselines, indicating that information from the overall training set and the meta-learning training paradigm could mutually reinforce one another. Moreover, being model-agnostic, our framework achieves significant performance gains, surpassing the baseline systems by approximate 1%.Comment: 11 pages, 6 figures, submitted to a journa

Are Ride-Hailing Services Safer Than Taxis? A Multivariate Spatial Approach with Accomodation of Exposure Uncertainty

Despite many research efforts on ride-hailing services and taxis, limited studies have compared the safety performance of the two modes. A major challenge is the need for reliable mode-specific exposure data to model their safety outcomes. Moreover, crash frequencies of the two modes by injury severities tend to be spatially and inherently correlated. To fully address these issues, this study proposes a novel multivariate conditional autoregressive model considering measurement errors in mode-specific exposures (MVCARME). More specially, a classical measurement error structure is used to accommodate the uncertainty of mode-specific exposures estimated, and a multivariate spatial specification is adopted to capture potential spatial and inherent correlations. The model estimation is accelerated by an integrated nest Laplace approximation method. The census tracts in the city of Chicago are set as the spatial analysis unit. The mode-specific exposures (vehicle-mile-traveled) in each census tract are estimated by trip assignments using ride-hailing and taxi trip data in 2019. The modeling results indicate that both ride-hailing crashes and taxi crashes are positively associated with transportation factors (e.g., vehicle-mile-traveled, mode-specific vehicle-mile-traveled, and traffic signal numbers), land use factors (i.e., number of educational and alcohol-related sites), and demographic factors (e.g., median household income, transit ratio, and walk ratio). By comparison, the proposed model outperforms the others (i.e., negative binomial models and multivariate conditional autoregressive model) by yielding the lowest deviance information criterion (DIC), Watanabe-Akaike information criterion (WAIC), mean absolute error (MAE), and root-mean-square error (RMSE). According to the results of t-tests, ride-hailing services are found to be prone to a higher risk of minor injury crashes compared with taxis, despite no significant difference between the risks of severe injury crashes. Methodologically, this study adds to the literature a robust safety evaluation approach for comparing crash risks of different modes. At the same time, practically, it provides researchers, practitioners, and policy-makers insights into the safety management of various mobility alternatives

Effects of Exogenous Melatonin on Body Mass Regulation and Hormone Concentrations in Eothenomys miletus

By regulating the pineal hormone, photoperiods affect many physiological characteristics in small mammals. Thus, melatonin might take part in the thermoregulation of seasonal variations in small mammals. This study determined the influence of melatonin treatment on thermogenic pattern, we measured body mass, thermogenic activities and hormone concentrations of Eothenomys miletus were given exogenous melatonin (MLT) for 28 days. The results shown that body mass was reduced significantly, whereas resting metabolic rate (RMR) and nonshivering thermogenesis (NST) increased at 28 days in MLT group compared to control group as well as the oxidative capacities of mitochondria in liver and brown adipose tissue (BAT) were enhanced; the contents of total and mitochodrial protein increased markedly. Melatonin treatment significantly increased the State 3, State 4 respiration of liver mitochondria, and the activity of cytochrome C oxidase (COX) in liver; but the α-glerocephasphate oxidase (α-PGO) capacity showed no differences during the acclimation in liver. Furthermore, the State 4 respiration, the activities of COX and α-PGO in BAT increased, respectively. The activity of thyroxin 5’-deiodinase ( T45’-DII) in BAT increased remarkably. The serum content of thyroxine (T 4) decreased, and that of tri-iodothyronine (T 3) increased. Moreover, serum leptin levels showed no significant differences in MLT group compared to control group. Together, these data indicate that melatonin enhances thermogenic capacity in E. miletus. Our results suggested that melatonin is potentially involved in the regulation of body mass, adaptive thermogenic capacity and hormone concentrations in E. miletus

Multi-Mode Dual-Polarized Cavity Backed Patch Antenna Array for 5G Mobile Devices

Abstract This paper proposes a dual‐polarised cavity backed patch antenna for next generation phased arrays for mobile handsets. The antenna exhibits multi‐band performance, allowing to cover the 5G bands n257 (26.5–29.5 GHz), n258 (24.25–27.5 GHz), n261 (27.5–28.35 GHz) and a portion of band n260 (37–40 GHz). Two orthogonal modes are excited in the substrate integrated waveguide (SIW) cavity and show similar impedance matching and symmetric radiation patterns. A parasitic element is introduced on top of the cavity and shifted off‐centre to improve the port‐to‐port isolation and guide the main beam. A parametric study of the antenna dimensions is conducted to verify the multi‐resonance operating mechanism. The design is validated by the measurements of the fabricated four‐element array. The array scanning angle is from −35° to 38° for V‐pol and from −29° to 31° for H‐pol at 28 GHz, where the realised gain raises from 8 to 12 dBi