Capacity estimation of two-dimensional channels using Sequential Monte Carlo

We derive a new Sequential-Monte-Carlo-based algorithm to estimate the capacity of two-dimensional channel models. The focus is on computing the noiseless capacity of the 2-D one-infinity run-length limited constrained channel, but the underlying idea is generally applicable. The proposed algorithm is profiled against a state-of-the-art method, yielding more than an order of magnitude improvement in estimation accuracy for a given computation time

Nested Sequential Monte Carlo Methods

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from sequences of probability distributions, even where the random variables are high-dimensional. NSMC generalises the SMC framework by requiring only approximate, properly weighted, samples from the SMC proposal distribution, while still resulting in a correct SMC algorithm. Furthermore, NSMC can in itself be used to produce such properly weighted samples. Consequently, one NSMC sampler can be used to construct an efficient high-dimensional proposal distribution for another NSMC sampler, and this nesting of the algorithm can be done to an arbitrary degree. This allows us to consider complex and high-dimensional models using SMC. We show results that motivate the efficacy of our approach on several filtering problems with dimensions in the order of 100 to 1 000.Comment: Extended version of paper published in Proceedings of the 32nd International Conference on Machine Learning (ICML), Lille, France, 201

Sequential Monte Carlo for Graphical Models

We propose a new framework for how to use sequential Monte Carlo (SMC) algorithms for inference in probabilistic graphical models (PGM). Via a sequential decomposition of the PGM we find a sequence of auxiliary distributions defined on a monotonically increasing sequence of probability spaces. By targeting these auxiliary distributions using SMC we are able to approximate the full joint distribution defined by the PGM. One of the key merits of the SMC sampler is that it provides an unbiased estimate of the partition function of the model. We also show how it can be used within a particle Markov chain Monte Carlo framework in order to construct high-dimensional block-sampling algorithms for general PGMs

Tricritical behavior of the massive chiral Gross-Neveu model

The phase diagram of the massive chiral Gross-Neveu model (the 1+1-dimensional Nambu-Jona-Lasinio model at large N) is investigated in the vicinity of the tricritical point. Using the derivative expansion, the grand canonical potential is cast into the form of a Ginzburg-Landau effective action. Minimization of this action by variational and numerical methods reveals both 1st and 2nd order phase transitions to a chiral crystal phase, separated by a tricritical line. These findings are contrasted to the massive Gross-Neveu model with discrete chiral symmetry where only 2nd order transitions have been observed.Comment: 10 pages, 10 figures; v2: More details about perturbation theory given, cf Eqs. (46-48

Surface reconstruction induced anisotropic energy landscape of bismuth monomers and dimers on the Si(001) surface

Spin qubits have attracted tremendous attention in the effort of building quantum computers over the years. Natural atomic scale candidates are group-V dopants in silicon, not only showing ultra-long lifetimes but also being compatible with current semiconductor technology. Nevertheless, bulk dopants are difficult to move with atomic precision, impeding the realization of desired structures for quantum computing. A solution is to place the atom on the surface which opens possibilities for atom level manipulations using scanning tunneling microscopy (STM). For this purpose, bismuth appears to be a good candidate. Here, we use ab-initio methods to study theoretically the adsorption of bismuth atoms on the Si(001) surface and investigate the adsorption sites and the transitions between them. We demonstrate the complex influence of the dimer row surface reconstruction on the energy landscape seen by a bismuth monomer and a dimer on the surface, and find anisotropic transition paths for movement on the surface. From a deposition simulation we obtain the expected occupation of adsorption sites. Our work lays the foundation for further application of bismuth atoms as qubits on silicon surfaces.Comment: 12 pages, 8 figure

The lid method for exhaustive exploration of metastable states of complex systems

The `lid' algorithm performs an exhaustive exploration of neighborhoods of local energy minima of energy landscapes. This paper describes an implementation of the algorithm, including issues of parallel performance and scalability. To illustrate the versatility of the approach and to stress the common features present in landscapes of quite different systems, we present selected results for 1) a spin glass, 2) a ferromagnet, 3) a covalent network model for glassy systems, and 4) a polymer. The exponential nature of the local density of states found in these systems and its relation to the ordering transition is briefly commented upon.Comment: RevTeX, 11 pages, 1 figur