Entanglement and quantum phase transitions

We examine several well known quantum spin models and categorize behavior of pairwise entanglement at quantum phase transitions. A unified picture on the connection between the entanglement and quantum phase transition is given.Comment: 4 pages, 3 figure

Chaos control in random Boolean networks by reducing mean damage percolation rate

Chaos control in Random Boolean networks is implemented by freezing part of the network to drive it from chaotic to ordered phase. However, controlled nodes are only viewed as passive blocks to prevent perturbation spread. This paper proposes a new control method in which controlled nodes can exert an active impact on the network. Controlled nodes and frozen values are deliberately selected according to the information of connection and Boolean functions. Simulation results show that the number of nodes needed to achieve control is largely reduced compared to previous method. Theoretical analysis is also given to estimate the least fraction of nodes needed to achieve control.Comment: 10 pages, 2 figure

Fidelity susceptibility and long-range correlation in the Kitaev honeycomb model

We study exactly both the ground-state fidelity susceptibility and bond-bond correlation function in the Kitaev honeycomb model. Our results show that the fidelity susceptibility can be used to identify the topological phase transition from a gapped A phase with Abelian anyon excitations to a gapless B phase with non-Abelian anyon excitations. We also find that the bond-bond correlation function decays exponentially in the gapped phase, but algebraically in the gapless phase. For the former case, the correlation length is found to be $1/\xi=2\sinh^{-1}[\sqrt{2J_z -1}/(1-J_z)]$, which diverges around the critical point $J_z=(1/2)^+$.Comment: 7 pages, 6 figure

Effect of gauge boson mass on the phase structure of QED3_{3}

Dynamical chiral symmetry breaking (DCSB) in QED$_{3}$ with finite gauge boson mass is studied in the framework of the rainbow approximation of Dyson-Schwinger equations. By adopting a simple gauge boson propagator ansatz at finite temperature, we first numerically solve the Dyson-Schwinger equation for the fermion self-energy to determine the chiral phase diagram of QED$_3$ with finite gauge boson mass at finite chemical potential and finite temperature, then we study the effect of the finite gauge mass on the phase diagram of QED$_3$. It is found that the gauge boson mass $m_{a}$ suppresses the occurrence of DCSB. The area of the region in the chiral phase diagram corresponding to DCSB phase decreases as the gauge boson mass $m_{a}$ increases. In particular, chiral symmetry gets restored when $m_{a}$ is above a certain critical value. In this paper, we use DCSB to describe the antiferromagnetic order and use the gauge boson mass to describe the superconducting order. Our results give qualitatively a physical picture on the competition and coexistence between antiferromagnetic order and superconducting orders in high temperature cuprate superconductors.Comment: 10 pages, 2 figure

Accepting higher morbidity in exchange for sacrificing fewer animals in studies developing novel infection-control strategies.

Preventing bacterial infections from becoming the leading cause of death by the year 2050 requires the development of novel, infection-control strategies, building heavily on biomaterials science, including nanotechnology. Pre-clinical (animal) studies are indispensable for this development. Often, animal infection outcomes bear little relation to human clinical outcome. Here, we review conclusions from pathogen-inoculum dose-finding pilot studies for evaluation of novel infection-control strategies in murine models. Pathogen-inoculum doses are generally preferred that produce the largest differences in quantitative infection outcome parameters between a control and an experimental group, without death or termination of animals due to having reached an inhumane end-point during the study. However, animal death may represent a better end-point for evaluation than large differences in outcome parameters or number of days over which infection persists. The clinical relevance of lower pre-clinical outcomes, such as bioluminescence, colony forming units (CFUs) retrieved or more rapid clearance of infection is unknown, as most animals cure infection without intervention, depending on pathogen-species and pathogen-inoculum dose administered. In human clinical practice, patients suffering from infection present to hospital emergency wards, frequently in life-threatening conditions. Animal infection-models should therefore use prevention of death and recurrence of infection as primary efficacy targets to be addressed by novel strategies. To compensate for increased animal morbidity and mortality, animal experiments should solely be conducted for pre-clinical proof of principle and safety. With the advent of sophisticated in vitro models, we advocate limiting use of animal models when exploring pathogenesis or infection mechanisms

The Euler Number of Bloch States Manifold and the Quantum Phases in Gapped Fermionic Systems

We propose a topological Euler number to characterize nontrivial topological phases of gapped fermionic systems, which originates from the Gauss-Bonnet theorem on the Riemannian structure of Bloch states established by the real part of the quantum geometric tensor in momentum space. Meanwhile, the imaginary part of the geometric tensor corresponds to the Berry curvature which leads to the Chern number characterization. We discuss the topological numbers induced by the geometric tensor analytically in a general two-band model. As an example, we show that the zero-temperature phase diagram of a transverse field XY spin chain can be distinguished by the Euler characteristic number of the Bloch states manifold in a (1+1)-dimensional Bloch momentum space

Density-functional fidelity approach to quantum phase transitions

We propose a new approach to quantum phase transitions in terms of the density-functional fidelity, which measures the similarity between density distributions of two ground states in parameter space. The key feature of the approach, as we will show, is that the density-functional fidelity can be measured easily in experiments. Both the validity and versatility of the approach are checked by the Lipkin-Meshkov-Glick model and the one-dimensional Hubbard model.Comment: 4 pages, 2 figures, submitted to Chin. Phys. Let

FastFlow: AI for Fast Urban Wind Velocity Prediction

Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet is in the quick quantitative evaluation of urban layouts for planning and design. In particular, urban designs typically involve complex trade-offs between multiple objectives, including limits on urban build-up and/or consideration of urban heat island effect. Hence, it can be beneficial to urban planners to have a fast surrogate model to predict urban characteristics of a hypothetical layout, e.g. pedestrian-level wind velocity, without having to run computationally expensive and time-consuming high-fidelity numerical simulations. This fast surrogate can then be potentially integrated into other design optimization frameworks, including generative models or other gradient-based methods. Here we present the use of CNNs for urban layout characterization that is typically done via high-fidelity numerical simulation. We further apply this model towards a first demonstration of its utility for data-driven pedestrian-level wind velocity prediction. The data set in this work comprises results from high-fidelity numerical simulations of wind velocities for a diverse set of realistic urban layouts, based on randomized samples from a real-world, highly built-up urban city. We then provide prediction results obtained from the trained CNN, demonstrating test errors of under 0.1 m/s for previously unseen urban layouts. We further illustrate how this can be useful for purposes such as rapid evaluation of pedestrian wind velocity for a potential new layout. It is hoped that this data set will further accelerate research in data-driven urban AI, even as our baseline model facilitates quantitative comparison to future methods

An experimental observation of geometric phases for mixed states using NMR interferometry

Examples of geometric phases abound in many areas of physics. They offer both fundamental insights into many physical phenomena and lead to interesting practical implementations. One of them, as indicated recently, might be an inherently fault-tolerant quantum computation. This, however, requires to deal with geometric phases in the presence of noise and interactions between different physical subsystems. Despite the wealth of literature on the subject of geometric phases very little is known about this very important case. Here we report the first experimental study of geometric phases for mixed quantum states. We show how different they are from the well understood, noiseless, pure-state case.Comment: 4 pages, 3 figure