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Gravity duals for logarithmic conformal field theories

Logarithmic conformal field theories with vanishing central charge describe systems with quenched disorder, percolation or dilute self-avoiding polymers. In these theories the energy momentum tensor acquires a logarithmic partner. In this talk we address the construction of possible gravity duals for these logarithmic conformal field theories and present two viable candidates for such duals, namely theories of massive gravity in three dimensions at a chiral point.

hep-th↗

Reconstruction algorithm in compressed sensing based on maximum a posteriori estimation

We propose a systematic method for constructing a sparse data reconstruction algorithm in compressed sensing at a relatively low computational cost for general observation matrix. It is known that the cost of l1-norm minimization using a standard linear programming algorithm is O(N^3). We show that this cost can be reduced to O(N^2) by applying the approach of posterior maximization. Furthermore, in principle, the algorithm from our approach is expected to achieve the widest successful reconstruction region, which is evaluated from theoretical argument. We also discuss the relation between the belief propagation-based reconstruction algorithm introduced in preceding works and our approach.

cs.IT↗

Simplifying Generalized Belief Propagation on Redundant Region Graphs

The cluster variation method has been developed into a general theoretical framework for treating short-range correlations in many-body systems after it was first proposed by Kikuchi in 1951. On the numerical side, a message-passing approach called generalized belief propagation (GBP) was proposed by Yedidia, Freeman and Weiss about a decade ago as a way of computing the minimal value of the cluster variational free energy and the marginal distributions of clusters of variables. However the GBP equations are often redundant, and it is quite a non-trivial task to make the GBP iteration converges to a fixed point. These drawbacks hinder the application of the GBP approach to finite-dimensional frustrated and disordered systems. In this work we report an alternative and simple derivation of the GBP equations starting from the partition function expression. Based on this derivation we propose a natural and systematic way of removing the redundance of the GBP equations. We apply the simplified generalized belief propagation (SGBP) equations to the two-dimensional and the three-dimensional ferromagnetic Ising model and Edwards-Anderson spin glass model. The numerical results confirm that the SGBP message-passing approach is able to achieve satisfactory performance on these model systems. We also suggest that a subset of the SGBP equations can be neglected in the numerical iteration process without affecting the final results.

cs.IT↗

Stability of topologically-protected quantum computing proposals as seen through spin glasses

Sensitivity to noise makes most of the current quantum computing schemes prone to error and nonscalable, allowing only for small proof-of-principle devices. Topologically-protected quantum computing aims at solving this problem by encoding quantum bits and gates in topological properties of the hardware medium that are immune to noise that does not impact the entire system at once. There are different approaches to achieve topological stability or active error correction, ranging from quasiparticle braidings to spin models and topological color codes. The stability of these proposals against noise can be quantified by their error threshold. This figure of merit can be computed by mapping the problem onto complex statistical-mechanical spin-glass models with local disorder on nontrival lattices that can have many-body interactions and are sometimes described by lattice gauge theories. The error threshold for a given source of error then represents the point in the temperature-disorder phase diagram where a stable symmetry-broken phase vanishes. An overview of the techniques used to estimate the error thresholds is given, as well as a summary of recent results on the stability of different topologically-protected quantum computing schemes to different error sources.

cond-mat.dis-nn↗

Packing hard spheres with short-range attraction in infinite dimension: Phase structure and algorithmic implications

We study, via the replica method of disordered systems, the packing problem of hard-spheres with a square-well attractive potential when the space dimensionality, d, becomes infinitely large. The phase diagram of the system exhibits reentrancy of the liquid-glass transition line, two distinct glass states and a glass-to-glass transition, much similar to what has been previously obtained by Mode-Coupling Theory, numerical simulations and experiments. The presence of the phase reentrance implies that for a suitable choice of the intensity and attraction range, high-density sphere packings more compact than the one corresponding to pure hard-spheres can be constructed in polynomial time in the number of particles (at fixed, large d) for packing fractions smaller than 6.5 d 2^{-d}. Although our derivation is not a formal mathematical proof, we believe it meets the standards of rigor of theoretical physics, and at this level of rigor it provides a small improvement of the lower bound on the sphere packing problem.

cond-mat.dis-nn↗

The hard-core model on random graphs revisited

We revisit the classical hard-core model, also known as independent set and dual to vertex cover problem, where one puts particles with a first-neighbor hard-core repulsion on the vertices of a random graph. Although the case of random graphs with small and very large average degrees respectively are quite well understood, they yield qualitatively different results and our aim here is to reconciliate these two cases. We revisit results that can be obtained using the (heuristic) cavity method and show that it provides a closed-form conjecture for the exact density of the densest packing on random regular graphs with degree K>=20, and that for K>16 the nature of the phase transition is the same as for large K. This also shows that the hard-code model is the simplest mean-field lattice model for structural glasses and jamming.

cond-mat.dis-nn↗

Performance of simulated annealing in p-spin glasses

We perform careful numerical simulations of slow Monte-Carlo annealings in the dense 3-body spin glass model and compare with the predictions from different theories: thresholds states, isocomplexity, following state. We conclude that while isocomplexity and following state both provide excellent agreement the numerical data, the influence of threshold states -- that is still the most commonly considered theory -- can be excluded from our data.

cond-mat.dis-nn↗

Zeros of the partition function and dynamical singularities in spin-glass systems

We study spin-glass systems characterized by continuous occurrence of singularities. The theory of Lee-Yang zeros is used to find the singularities. By using the replica method in mean-field systems, we show that two-dimensional distributions of zeros of the partition function in a complex parameter plane are characteristic feature of random systems. The results of several models indicate that the concept of chaos in the spin-glass state is different from that of the replica symmetry breaking. We discuss that a chaotic phase at imaginary temperature is different from the spin-glass phase and is accessible by quantum dynamics in a quenching protocol.

cond-mat.stat-mech↗

Spatial properties of $π-π$ conjugated network in semicrystalline polymer thin films studied by intensity x-ray cross-correlation functions

We present results of x-ray study of spatial properties of $π-π$ conjugated networks in polymer thin films. We applied the x-ray cross-correlation analysis to x-ray scattering data from blends of poly(3-hexylthiophene) (P3HT) and gold nanoparticles. The Fourier spectra of the intensity cross-correlation functions for different films contain non-zero components of orders $n=2,4$ and $6$ measuring the degree of structural order in the system.

cond-mat.soft↗

Eigenvalue repulsion estimates and some applications for the one-dimensional Anderson model

We show that the spacing between eigenvalues of the discrete 1D Hamiltonian with arbitrary potentials which are bounded, and with Dirichlet or Neumann Boundary Conditions is bounded away from zero. We prove an explicit lower bound, given by $Ce^{-bN}$, where $N$ is the lattice size, and $C$ and $b$ are some finite constants. In particular, the spectra of such Hamiltonians have no degenerate eigenvalues. As applications we show that to leading order in the coupling, the solution of a nonlinearly perturbed Anderson model in one-dimension (on the lattice) remains exponentially localized, in probability and average sense for initial conditions given by a unique eigenfunction of the linear problem. We also bound the derivative of the eigenfunctions of the linear Anderson model with respect to a potential change.

cond-mat.dis-nn↗

Flux conservation in coherent backscattering and weak localisation of light

The standard theoretical description of coherent backscattering, accord- ing to which maximally crossed diagrams accounting for interference between counter- propagating path amplitudes are added on top of the incoherent background, violates the fundamental condition of flux conservation. In contrast to predictions of previous theories, we show that including maximally crossed diagrams with one additional scat- tering event does not restore flux conservation. Instead, we propose that the latter is recovered when treating the effects of coherent backscattering and weak localisation in a unified framework. On the basis of this framework, we demonstrate explicitly flux conservation in leading order of the weak disorder parameter 1/(kl).

cond-mat.dis-nn↗

Entropy landscape of solutions in the binary perceptron problem

The statistical picture of the solution space for a binary perceptron is studied. The binary perceptron learns a random classification of input random patterns by a set of binary synaptic weights. The learning of this network is difficult especially when the pattern (constraint) density is close to the capacity, which is supposed to be intimately related to the structure of the solution space. The geometrical organization is elucidated by the entropy landscape from a reference configuration and of solution-pairs separated by a given Hamming distance in the solution space. We evaluate the entropy at the annealed level as well as replica symmetric level and the mean field result is confirmed by the numerical simulations on single instances using the proposed message passing algorithms. From the first landscape (a random configuration as a reference), we see clearly how the solution space shrinks as more constraints are added. From the second landscape of solution-pairs, we deduce the coexistence of clustering and freezing in the solution space.

cond-mat.dis-nn↗

Susceptible-Infected-Recovered model on Euclidean network

We consider the Susceptible-Infected-Recovered (SIR) epidemic model on a Euclidean network in one dimension in which nodes at a distance $l$ are connected with probability $P(l) \propto l^{-δ}$ in addition to nearest neighbors. The topology of the network changes as $δ$ is varied and its effect on the SIR model is studied. $R(t)$, the recovered fraction of population up to time $t$, and $τ$, the total duration of the epidemic are calculated for different values of the infection probability $q$ and $δ$. A threshold behavior is observed for all $δ$ up to $δ\approx 2.0$; above the threshold value $q = q_c$, the saturation value $R_{sat}$ attains a finite value. Both $R_{sat}$ and $τ$ show scaling behavior in a finite system of size $N$; $R_{sat} \sim N^{-β/{\tildeν}} g_1[(q-q_c)N^{1/{\tilde ν}}]$ and $τ\sim N^{μ/{\tildeν}} g_2[(q-q_c)N^{1/\tildeν}]$. $q_c$ is constant for $0 \leq δ< 1$ and increases with $δ$ for $1<δ\lesssim 2$. Mean field behavior is seen up to $δ\approx 1.3$; weak dependence on $δ$ is observed beyond this value of $δ$.The distribution of the outbreak sizes is also estimated and found to be unimodal for $q < q_c$ and bimodal for $q > q_c$. The results are compared to static percolation phenomenaand also to mean field results for finite systems. Discussions on the properties of the Euclidean network are made in the light of the present results.

physics.soc-ph↗

Bose-Einstein condensation in a minimal inhomogeneous system

We study the effects of repulsive interaction and disorder on Bosons in a two-site Bose-Hubbard system, which provides a simple model of the dirty boson problem. By comparison with exact numerical results, we demonstrate how a straightforward application of the Bogoliubov approximation fails even to deliver a qualitatively correct picture: It wrongly predicts an increase of the condensate depletion due to disorder. We show that, in the presence of disorder, the noncommutative character of the condensate operator has to be retained for a correct description of the system.

cond-mat.quant-gas↗

Universal statistics of longest lasting records of random walks and Lévy flights

We study the record statistics of random walks after $n$ steps, $x_0, x_1,\ldots, x_n$, with arbitrary symmetric and continuous distribution $p(η)$ of the jumps $η_i = x_i - x_{i-1}$. We consider the age of the records, i.e. the time up to which a record survives. Depending on how the age of the current last record is defined, we propose three distinct sequences of ages (indexed by $α$ = I, II, III) associated to a given sequence of records. We then focus on the longest lasting record, which is the longest element among this sequence of ages. To characterize the statistics of these longest lasting records, we compute: (i) the probability that the record of the longest age is broken at step $n$, denoted by $Q^α(n)$, which we call the probability of record breaking and: (ii) the duration of the longest lasting record, $\ell_{\max}^α(n)$. We show that both $Q^α(n)$ and the full statistics of $\ell_{\max}^α(n)$ are universal, i.e. independent of the jump distribution $p(η)$. We compute exactly the large $n$ asymptotic behaviors of $Q^α(n)$ as well as $\langle \ell_{\max}^α(n)\rangle$ (when it exists) and show that each case gives rise to a different universal constant associated to random walks (including Lévy flights). While two of them appeared before in the excursion theory of Brownian motion, for which we provide here a simpler derivation, the third case gives rise to a non-trivial new constant $C^{\rm III} = 0.241749 \ldots$ associated to the records of random walks. Other observables characterizing the ages of the records, exhibiting an interesting universal behavior, are also discussed.

cond-mat.stat-mech↗

Diffusion in periodic, correlated random forcing landscapes

We study the dynamics of a Brownian particle in a strongly correlated quenched random potential defined as a periodically-extended (with period $L$) finite trajectory of a fractional Brownian motion with arbitrary Hurst exponent $H \in (0,1)$. While the periodicity ensures that the ultimate long-time behavior is diffusive, the generalised Sinai potential considered here leads to a strong logarithmic confinement of particle trajectories at intermediate times. These two competing trends lead to dynamical frustration and result in a rich statistical behavior of the diffusion coefficient $D_L$: Although one has the typical value $D^{\rm typ}_L \sim \exp(-βL^H)$, we show via an exact analytical approach that the positive moments ($k>0$) scale like $\langle D^k_L \rangle \sim \exp{[-c' (k βL^{H})^{1/(1+H)}]}$, and the negative ones as $\langle D^{-k}_L \rangle \sim \exp(a' (k βL^{H})^2)$, $c'$ and $a'$ being numerical constants and $β$ the inverse temperature. These results demonstrate that $D_L$ is strongly non-self-averaging. We further show that the probability distribution of $D_L$ has a log-normal left tail and a highly singular, one-sided log-stable right tail reminiscent of a Lifshitz singularity.

cond-mat.stat-mech↗

Out-of-equilibrium dynamical mean-field equations for the perceptron model

Perceptrons are the building blocks of many theoretical approaches to a wide range of complex systems, ranging from neural networks and deep learning machines, to constraint satisfaction problems, glasses and ecosystems. Despite their applicability and importance, a detailed study of their Langevin dynamics has never been performed yet. Here we derive the mean-field dynamical equations that describe the continuous random perceptron in the thermodynamic limit, in a very general setting with arbitrary noise and friction kernels, not necessarily related by equilibrium relations. We derive the equations in two ways: via a dynamical cavity method, and via a path-integral approach in its supersymmetric formulation. The end point of both approaches is the reduction of the dynamics of the system to an effective stochastic process for a representative dynamical variable. Because the perceptron is formally very close to a system of interacting particles in a high dimensional space, the methods we develop here can be transferred to the study of liquid and glasses in high dimensions. Potentially interesting applications are thus the study of the glass transition in active matter, the study of the dynamics around the jamming transition, and the calculation of rheological properties in driven systems.

cond-mat.dis-nn↗

Strong Disorder Real-Space Renormalization for the Many-Body-Localized phase of random Majorana models

For the Many-Body-Localized phase of random Majorana models, a general Strong Disorder Real-Space Renormalization procedure known as RSRG-X [D. Pekker, G. Refael, E. Altman, E. Demler and V. Oganesyan, Phys. Rev. X 4, 011052 (2014)] is described to produce the whole set of excited states, via the iterative construction of the Local Integrals of Motion (LIOMs). The RG rules are then explicitly derived for arbitrary quadratic Hamiltonians (free-fermions models) and for the Kitaev chain with local interactions involving even numbers of consecutive Majorana fermions. The emphasis is put on the advantages of the Majorana language over the usual quantum spin language to formulate unified RSRG-X rules.

cond-mat.dis-nn↗