2,868 research outputs found
Tunable Quantum Beam Splitters for Coherent Manipulation of a Solid-State Tripartite Qubit System
Coherent control of quantum states is at the heart of implementing
solid-state quantum processors and testing quantum mechanics at the macroscopic
level. Despite significant progress made in recent years in controlling single-
and bi-partite quantum systems, coherent control of quantum wave function in
multipartite systems involving artificial solid-state qubits has been hampered
due to the relatively short decoherence time and lacking of precise control
methods. Here we report the creation and coherent manipulation of quantum
states in a tripartite quantum system, which is formed by a superconducting
qubit coupled to two microscopic two-level systems (TLSs). The avoided
crossings in the system's energy-level spectrum due to the qubit-TLS
interaction act as tunable quantum beam splitters of wave functions. Our result
shows that the Landau-Zener-St\"{u}ckelberg interference has great potential in
the precise control of the quantum states in the tripartite system.Comment: 24 pages, 3 figure
Adversarial Attack and Defense on Graph Data: A Survey
Deep neural networks (DNNs) have been widely applied to various applications
including image classification, text generation, audio recognition, and graph
data analysis. However, recent studies have shown that DNNs are vulnerable to
adversarial attacks. Though there are several works studying adversarial attack
and defense strategies on domains such as images and natural language
processing, it is still difficult to directly transfer the learned knowledge to
graph structure data due to its representation challenges. Given the importance
of graph analysis, an increasing number of works start to analyze the
robustness of machine learning models on graph data. Nevertheless, current
studies considering adversarial behaviors on graph data usually focus on
specific types of attacks with certain assumptions. In addition, each work
proposes its own mathematical formulation which makes the comparison among
different methods difficult. Therefore, in this paper, we aim to survey
existing adversarial learning strategies on graph data and first provide a
unified formulation for adversarial learning on graph data which covers most
adversarial learning studies on graph. Moreover, we also compare different
attacks and defenses on graph data and discuss their corresponding
contributions and limitations. In this work, we systemically organize the
considered works based on the features of each topic. This survey not only
serves as a reference for the research community, but also brings a clear image
researchers outside this research domain. Besides, we also create an online
resource and keep updating the relevant papers during the last two years. More
details of the comparisons of various studies based on this survey are
open-sourced at
https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date
information, please check our Github repository:
https://github.com/YingtongDou/graph-adversarial-learning-literatur
Distributed H∞-consensus filtering in sensor networks with multiple missing measurements: The finite-horizon case
The official published version of the article can be found at the link below.This paper is concerned with a new distributed H∞-consensus filtering problem over a finite-horizon for sensor networks with multiple missing measurements. The so-called H∞-consensus performance requirement is defined to quantify bounded consensus regarding the filtering errors (agreements) over a finite-horizon. A set of random variables are utilized to model the probabilistic information missing phenomena occurring in the channels from the system to the sensors. A sufficient condition is first established in terms of a set of difference linear matrix inequalities (DLMIs) under which the expected H∞-consensus performance constraint is guaranteed. Given the measurements and estimates of the system state and its neighbors, the filter parameters are then explicitly parameterized by means of the solutions to a certain set of DLMIs that can be computed recursively. Subsequently, two kinds of robust distributed H∞-consensus filters are designed for the system with norm-bounded uncertainties and polytopic uncertainties. Finally, two numerical simulation examples are used to demonstrate the effectiveness of the proposed distributed filters design scheme.This work was supported in part by the Engineering and Physical Sciences Research Council (EPSRC) of the UK under Grant GR/S27658/01, the Royal Society of the UK, and the Alexander von Humboldt Foundation of Germany
Quantum oscillations in adsorption energetics of atomic oxygen on Pb(111) ultrathin films: A density-functional theory study
Using first-principles calculations, we have systematically studied the
quantum size effects of ultrathin Pb(111) films on the adsorption energies and
diffusion energy barriers of oxygen atoms. For the on-surface adsorption of
oxygen atoms at different coverages, all the adsorption energies are found to
show bilayer oscillation behaviors. It is also found that the work function of
Pb(111) films still keeps the bilayer-oscillation behavior after the adsorption
of oxygen atoms, with the values being enlarged by 2.10 to 2.62 eV. For the
diffusion and penetration of the adsorbed oxygen atoms, it is found that the
most energetically favored paths are the same on different Pb(111) films. And
because of the modulation of quantum size effects, the corresponding energy
barriers are all oscillating with a bilayer period on different Pb(111) films.
Our studies indicate that the quantum size effect in ultrathin metal films can
modulate a lot of processes during surface oxidation
Study on criterion of fabricating columnar dendrite structure DZ466 superalloy based on LMC process
Performance Analysis and Comparison of Non-ideal Wireless PBFT and RAFT Consensus Networks in 6G Communications
Due to advantages in security and privacy, blockchain is considered a key
enabling technology to support 6G communications. Practical Byzantine Fault
Tolerance (PBFT) and RAFT are seen as the most applicable consensus mechanisms
(CMs) in blockchain-enabled wireless networks. However, previous studies on
PBFT and RAFT rarely consider the channel performance of the physical layer,
such as path loss and channel fading, resulting in research results that are
far from real networks. Additionally, 6G communications will widely deploy
high-frequency signals such as terahertz (THz) and millimeter wave (mmWave),
while performances of PBFT and RAFT are still unknown when these signals are
transmitted in wireless PBFT or RAFT networks. Therefore, it is urgent to study
the performance of non-ideal wireless PBFT and RAFT networks with THz and
mmWave signals, to better make PBFT and RAFT play a role in the 6G era. In this
paper, we study and compare the performance of THz and mmWave signals in
non-ideal wireless PBFT and RAFT networks, considering Rayleigh Fading (RF) and
close-in Free Space (FS) reference distance path loss. Performance is evaluated
by five metrics: consensus success rate, latency, throughput, reliability gain,
and energy consumption. Meanwhile, we find and derive that there is a maximum
distance between two nodes that can make CMs inevitably successful, and it is
named the active distance of CMs. The research results not only analyze the
performance of non-ideal wireless PBFT and RAFT networks, but also provide
important references for the future transmission of THz and mmWave signals in
PBFT and RAFT networks.Comment: arXiv admin note: substantial text overlap with arXiv:2303.1575
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