365,878 research outputs found
Simulation of Quantum Computation: A deterministic event-based approach
We demonstrate that locally connected networks of machines that have
primitive learning capabilities can be used to perform a deterministic,
event-based simulation of quantum computation. We present simulation results
for basic quantum operations such as the Hadamard and the controlled-NOT gate,
and for seven-qubit quantum networks that implement Shor's numbering factoring
algorithm.Comment: J. Comp. Theor. Nanoscience (in press); http://www.compphys.net/dl
Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics
Finding reduced models of spatially-distributed chemical reaction networks
requires an estimation of which effective dynamics are relevant. We propose a
machine learning approach to this coarse graining problem, where a maximum
entropy approximation is constructed that evolves slowly in time. The dynamical
model governing the approximation is expressed as a functional, allowing a
general treatment of spatial interactions. In contrast to typical machine
learning approaches which estimate the interaction parameters of a graphical
model, we derive Boltzmann-machine like learning algorithms to estimate
directly the functionals dictating the time evolution of these parameters. By
incorporating analytic solutions from simple reaction motifs, an efficient
simulation method is demonstrated for systems ranging from toy problems to
basic biologically relevant networks. The broadly applicable nature of our
approach to learning spatial dynamics suggests promising applications to
multiscale methods for spatial networks, as well as to further problems in
machine learning
Simulating Knowledge-Generation and -Distribution Processes in Innovation Collaborations and Networks
An agent-based simulation model representing a theory of the dynamic processes involved in innovation in modern knowledge-based industries is described. The agent-based approach al-lows the representation of heterogeneous agents that have individual and varying stocks of knowledge. The simulation is able to model uncertainty, historical change, effect of failure on the agent population, and agent learning from experience, from individual research and from partners and collaborators. The aim of the simulation exercises is to show that the artificial innovation networks show certain characteristics they share with innovation networks in knowledge intensive industries and which are difficult to be integrated in traditional models of industrial economics.innovation networks, agent-based modelling, scale free networks
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