output
20022011
most citedExponential algorithmic speedup by quantum walk

836 citations

Showing cond-mat.stat-mechShow all

8 papers · 1 filter

cond-mat.stat-mech201013 cited

Random Sequential Renormalization of Networks I: Application to Critical Trees

Golnoosh Bizhani, Vishal Sood, Maya Paczuski +1

We introduce the concept of Random Sequential Renormalization (RSR) for arbitrary networks. RSR is a graph renormalization procedure that locally aggregates nodes to produce a coar…

cond-mat.stat-mech20096 cited

Random sampling vs. exact enumeration of attractors in random Boolean networks

Andrew Berdahl, Amer Shreim, Vishal Sood +2

We clarify the effect different sampling methods and weighting schemes have on the statistics of attractors in ensembles of random Boolean networks (RBNs). We directly measure cycl…

cond-mat.stat-mech20083 cited

Cooling ultracold bosons in optical lattices by spectral transform

David L. Feder

It is shown theoretically how to directly obtain the energy distribution of a weakly interacting gas of bosons confined in an optical lattice in the tight-binding limit. This is ac…

cond-mat.stat-mech200827 cited

Reinforced walks in two and three dimensions

Jacob G. Foster, Peter Grassberger, Maya Paczuski

In probability theory, reinforced walks are random walks on a lattice (or more generally a graph) that preferentially revisit neighboring `locations' (sites or bonds) that have bee…

cond-mat.stat-mech20078 cited

Complex Network Analysis of State Spaces for Random Boolean Networks

Amer Shreim, Andrew Berdahl, Vishal Sood +2

We apply complex network analysis to the state spaces of random Boolean networks (RBNs). An RBN contains Boolean elements each with inputs. A directed state space network (…

cond-mat.stat-mech200750 cited

Localization Transition of Biased Random Walks on Random Networks

Vishal Sood, Peter Grassberger

We study random walks on large random graphs that are biased towards a randomly chosen but fixed target node. We show that a critical bias strength b_c exists such that most walks…