Scaling laws for precision in quantum interferometry and bifurcation landscape of optimal state

Phase precision in optimal 2-channel quantum interferometry is studied in the limit of large photon number $N\gg 1$, for losses occurring in either one or both channels. For losses in one channel an optimal state undergoes an intriguing sequence of local bifurcations as the losses or the number of photons increase. We further show that fixing the loss paramater determines a scale for quantum metrology -- a crossover value of the photon number $N_c$ beyond which the supra-classical precision is progressively lost. For large losses the optimal state also has a different structure from those considered previously.Comment: 4 pages, 3 figures, v3 is modified in response to referee comment

Cascading traffic jamming in a two-dimensional Motter and Lai model

We study the cascading traffic jamming on a two-dimensional random geometric graph using the Motter and Lai model. The traffic jam is caused by a localized attack incapacitating circular region or a line of a certain size, as well as a dispersed attack on an equal number of randomly selected nodes. We investigate if there is a critical size of the attack above which the network becomes completely jammed due to cascading jamming, and how this critical size depends on the average degree $\langle k\rangle$ of the graph, on the number of nodes $N$ in the system, and the tolerance parameter $\alpha$ of the Motter and Lai model.Comment: 14 pages, 9 figure

Ground State Properties of Simple Elements from GW Calculations

A novel self-consistent implementation of Hedin's GW perturbation theory is introduced. This finite-temperature method uses Hartree-Fock wave functions to represent Green's function. GW equations are solved with full potential linear augmented plane wave (FLAPW) method at each iteration of a self-consistent cycle. With our approach we are able to calculate total energy as a function of the lattice parameter. Ground state properties calculated for Na, Al, and Si compare well with experimental data.Comment: 4 pages, 3figure

Network Overload due to Massive Attacks

We study the cascading failure of networks due to overload, using the betweenness centrality of a node as the measure of its load following the Motter and Lai model. We study the fraction of survived nodes at the end of the cascade $p_f$ as function of the strength of the initial attack, measured by the fraction of nodes $p$, which survive the initial attack for different values of tolerance $\alpha$ in random regular and Erd\"os-Renyi graphs. We find the existence of first order phase transition line $p_t(\alpha)$ on a $p-\alpha$ plane, such that if $p <p_t$ the cascade of failures lead to a very small fraction of survived nodes $p_f$ and the giant component of the network disappears, while for $p>p_t$, $p_f$ is large and the giant component of the network is still present. Exactly at $p_t$ the function $p_f(p)$ undergoes a first order discontinuity. We find that the line $p_t(\alpha)$ ends at critical point $(p_c,\alpha_c)$ ,in which the cascading failures are replaced by a second order percolation transition. We analytically find the average betweenness of nodes with different degrees before and after the initial attack, investigate their roles in the cascading failures, and find a lower bound for $p_t(\alpha)$. We also study the difference between a localized and random attacks

Interdependent networks with correlated degrees of mutually dependent nodes

We study a problem of failure of two interdependent networks in the case of correlated degrees of mutually dependent nodes. We assume that both networks (A and B) have the same number of nodes $N$ connected by the bidirectional dependency links establishing a one-to-one correspondence between the nodes of the two networks in a such a way that the mutually dependent nodes have the same number of connectivity links, i.e. their degrees coincide. This implies that both networks have the same degree distribution $P(k)$. We call such networks correspondently coupled networks (CCN). We assume that the nodes in each network are randomly connected. We define the mutually connected clusters and the mutual giant component as in earlier works on randomly coupled interdependent networks and assume that only the nodes which belong to the mutual giant component remain functional. We assume that initially a $1-p$ fraction of nodes are randomly removed due to an attack or failure and find analytically, for an arbitrary $P(k)$, the fraction of nodes $\mu(p)$ which belong to the mutual giant component. We find that the system undergoes a percolation transition at certain fraction $p=p_c$ which is always smaller than the $p_c$ for randomly coupled networks with the same $P(k)$. We also find that the system undergoes a first order transition at $p_c>0$ if $P(k)$ has a finite second moment. For the case of scale free networks with $2<\lambda \leq 3$, the transition becomes a second order transition. Moreover, if $\lambda<3$ we find $p_c=0$ as in percolation of a single network. For $\lambda=3$ we find an exact analytical expression for $p_c>0$. Finally, we find that the robustness of CCN increases with the broadness of their degree distribution.Comment: 18 pages, 3 figure