Robust ground-state energy estimation under depolarizing noise

We present a novel ground-state energy estimation algorithm that is robust under global depolarizing error channels. Building upon the recently developed Quantum Exponential Least Squares (QCELS) algorithm [Ding, Lin, PRX Quantum, 4, 020331, 2023], our new approach incorporates significant advancements to ensure robust estimation while maintaining a polynomial cost in precision. By leveraging the spectral gap of the Hamiltonian effectively, our algorithm overcomes limitations observed in previous methods like quantum phase estimation (QPE) and robust phase estimation (RPE). Going beyond global depolarizing error channels, our work underscores the significance and practical advantages of utilizing randomized compiling techniques to tailor quantum noise towards depolarizing error channels. Our research demonstrates the feasibility of ground-state energy estimation in the presence of depolarizing noise, offering potential advancements in error correction and algorithmic-level error mitigation for quantum algorithms.Comment: 35 pages, 8 figures. The first two authors contributed equally to this wor

Blockchain-Based Identity Authentication Oriented to Multi-Cluster UAV Networking

Unmanned Aerial Vehicle (UAV) networking is increasingly used in field environments such as power inspection, agricultural plant protection, and emergency rescue. To guarantee UAV networking security, UAV identity authentication attracts wide attention, especially in the field environment without perfect infrastructure. Some blockchain-based UAV identity authentication solutions are proposed to establish decentralized and trusted authentication systems without relying on infrastructure. However, these solutions do not support disconnected UAV reconnection or even disband a cluster directly after its head UAV disconnection, which compromises cluster robustness and task result integrity. In this paper, we propose a blockchain-based identity authentication solution oriented to multi-cluster UAV networking with a UAV disconnection mechanism and a task result backup mechanism. Specifically, we build a blockchain maintained by head UAVs of all clusters, managing identity information to guarantee the security of decentralized identity management. The UAV disconnection mechanism permits a verified distributed UAV reconnection to ensure the robustness of the UAV cluster, and on this basis, the task result backup mechanism ensures the integrity of the task results stored in a cluster even any UAV disconnection. Finally, extensive experimental results prove the superiority of our solutions in terms of robustness, integrity, delay, and energy consumption

Adversarial Attacks and Defenses in Machine Learning-Powered Networks: A Contemporary Survey

Adversarial attacks and defenses in machine learning and deep neural network have been gaining significant attention due to the rapidly growing applications of deep learning in the Internet and relevant scenarios. This survey provides a comprehensive overview of the recent advancements in the field of adversarial attack and defense techniques, with a focus on deep neural network-based classification models. Specifically, we conduct a comprehensive classification of recent adversarial attack methods and state-of-the-art adversarial defense techniques based on attack principles, and present them in visually appealing tables and tree diagrams. This is based on a rigorous evaluation of the existing works, including an analysis of their strengths and limitations. We also categorize the methods into counter-attack detection and robustness enhancement, with a specific focus on regularization-based methods for enhancing robustness. New avenues of attack are also explored, including search-based, decision-based, drop-based, and physical-world attacks, and a hierarchical classification of the latest defense methods is provided, highlighting the challenges of balancing training costs with performance, maintaining clean accuracy, overcoming the effect of gradient masking, and ensuring method transferability. At last, the lessons learned and open challenges are summarized with future research opportunities recommended.Comment: 46 pages, 21 figure

Correlation-induced symmetry-broken states in large-angle twisted bilayer graphene on MoS2

Strongly correlated states are commonly emerged in twisted bilayer graphene (TBG) with magic-angle, where the electron-electron (e-e) interaction U becomes prominent relative to the small bandwidth W of the nearly flat band. However, the stringent requirement of this magic angle makes the sample preparation and the further application facing great challenges. Here, using scanning tunneling microscopy (STM) and spectroscopy (STS), we demonstrate that the correlation-induced symmetry-broken states can also be achieved in a 3.45{\deg} TBG, via engineering this non-magic-angle TBG into regimes of U/W > 1. We enhance the e-e interaction through controlling the microscopic dielectric environment by using a MoS2 substrate. Simultaneously, the bandwidth of the low-energy van Hove singularity (VHS) peak is reduced by enhancing the interlayer coupling via STM tip modulation. When partially filled, the VHS peak exhibits a giant splitting into two states flanked the Fermi level and shows a symmetry-broken LDOS distribution with a stripy charge order, which confirms the existence of strong correlation effect in our 3.45{\deg} TBG. Our result paves the way for the study and application of the correlation physics in TBGs with a wider range of twist angle

Discharging behavior of a fixed-bed thermochemical reactor under different charging conditions: modelling and experimental validation

Thermochemical heat storage has attracted significant attention in recent years due to potential advantages associated with very high-energy density at the material scale and its suitability for long-duration energy storage because of almost zero loss during storage. Despite the potential, thermochemical heat storage technologies are still in the early stage of development and little has been reported on thermochemical reactors. In this paper, our recent work on the charging and discharging behavior of a fixed-bed thermochemical reactor is reported. Silica gels were used as the sorbent for the experimental work. An effective model was established to numerically study the effect of different charging conditions on the discharging behavior of the reactor, which was found to have a maximum deviation of 10.08% in terms of the root mean square error compared with the experimental results. The experimentally validated modelling also showed that the discharging temperature lift increased by 5.84 times by changing the flow direction of the air in the discharging process when the charging level was at 20%. At a charging termination temperature of 51.25 °C, the maximum discharging temperature was increased by 2.35 °C by reducing the charging flow velocity from 0.64 m/s to 0.21 m/s. An increase in the charging temperature and a decrease in the air humidity increased the maximum discharging outlet temperature lift by 3.37 and 1.89 times, respectively

A Truncated IL‐17RC Peptide Ameliorates Synovitis and Bone Destruction of Arthritic Mice

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/134880/1/adhm201600668_am.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134880/2/adhm201600668-sup-0001-S1.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/134880/3/adhm201600668.pd