Quantum annealing with ultracold atoms in a multimode optical resonator

A dilutely filled $N$-site optical lattice near zero temperature within a high-$Q$ multimode cavity can be mapped to a spin ensemble with tailorable interactions at all length scales. The effective full site to site interaction matrix can be dynamically controlled by the application of up to $N(N+1)/2$ laser beams of suitable geometry, frequency and power, which allows for the implementation of quantum annealing dynamics relying on the all-to-all effective spin coupling controllable in real time. Via an adiabatic sweep starting from a superfluid initial state one can find the lowest energy stationary state of this system. As the cavity modes are lossy, errors can be amended and the ground state can still be reached even from a finite temperature state via ground state cavity cooling. The physical properties of the final atomic state can be directly and almost non-destructively read off from the cavity output fields. As example we simulate a quantum Hopfield associative memory scheme.Comment: 11 pages, 7 figures; extended paper: added figures, further explanations and appendice

Advances in quantum machine learning

Here we discuss advances in the field of quantum machine learning. The following document offers a hybrid discussion; both reviewing the field as it is currently, and suggesting directions for further research. We include both algorithms and experimental implementations in the discussion. The field's outlook is generally positive, showing significant promise. However, we believe there are appreciable hurdles to overcome before one can claim that it is a primary application of quantum computation.Comment: 38 pages, 17 Figure

Evolutionary robotics and neuroscience

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Food science applications and international trends of artificial neural networks

Recently, research has been focusing increasingly on the system of artificial neural networks, and its results are used in many places by industrial practices. The success of these networks lies in their ability to recognize the complex relationships and patterns in data, as well as to predict unknown samples, thus enabling value and category predictions with high certainty. Artificial neural networks are very efficient tools for modeling non-linear trends within data. In many cases, they perform well where traditional statistical tools provide unsatisfactory results or unable to solve a given research problem. In our work, the operation principle and structure (topol-ogy) of artificial neural networks are summarized, as well as the classification and application possibilities of the networks. The latest food science applications are presented separately, based on the usage type (prediction, classification, optimiza-tion). Results show that artificial neural networks possess many beneficial properties, making them especially suitable for solving food science tasks

A stable and accurate control-volume technique based on integrated radial basis function networks for fluid-flow problems

Radial basis function networks (RBFNs) have been widely used in solving partial differential equations as they are able to provide fast convergence. Integrated RBFNs have the ability to avoid the problem of reduced convergence-rate caused by differentiation. This paper is concerned with the use of integrated RBFNs in the context of control-volume discretisations for the simulation of fluid-flow problems. Special attention is given to (i) the development of a stable high-order upwind scheme for the convection term and (ii) the development of a local high-order approximation scheme for the diffusion term. Benchmark problems including the lid-driven triangular-cavity flow are employed to validate the present technique. Accurate results at high values of the Reynolds number are obtained using relatively-coarse grids

The characterisation of multiple defects in components using artificial neural networks

This thesis investigates the use of artificial neural networks (ANNs) as a means of processing signals from non-destructive tests, to characterise defects and provide more information regarding the condition of the component than would otherwise be possible for an operator to obtain from the test data. ANNs are used both as pattern classifiers and as function approximators. In the first part of the thesis, finite element analysis was carried out on a simple component containing a single defect modelled as a void, simulating three kinds of non-destructive test: an impact method that sent a stress wave through the component, an analysis of natural frequencies, and an ultrasonic pulse-echo method. The inputs to the ANNs were data from the numerical model, and the outputs were the x and y co-ordinates of the defect in the case of the impact and frequency methods, and the size and distance to the defect in the case of the ultrasonic method. Very good accuracy was observed in all three methods. Experimental validation of the ultrasonic method was carried out, and the ANNs returned accurate outputs for the position and size of a circular hole in a steel plate when presented with experimental data. When the ANNs were presented with noisy input data, their reduction in accuracy was small in comparison with published data from similar studies. In the second part of the thesis, the case of two defects lying within one wavelength of each other was considered, where the reflected ultrasonic waves from each defect overlapped, partially cancelling each other out and reducing the overall amplitude. A novel ANN-based approach was developed to decouple the overlapping signals, characterising each defect in terms of its position and size. Optimisation of the ANN architecture was carried out to maximise the ability of the ANN to generalise when presented with previously unseen data. Finally, an ANN-based general defect characterisation ‘expert system’ is presented, using data from an ultrasonic test as its input, and classifying cases according to the number of defects present. The system then characterised the defects present in the component in terms of their location and size, providing more information regarding the component’s condition than would be possible by existing techniques

Prediction of the response under impact of steel armours using a multilayer perceptron

8 pages, 10 figures.This article puts forward the results obtained when using a neural network as an alternative to classical methods (simulation and experimental testing) in the prediction of the behaviour of steel armours against high-speed impacts. In a first phase, a number of impact cases are randomly generated, varying the values of the parameters which define the impact problem (radius, length and velocity of the projectile; thickness of the protection). After simulation of each case using a finite element code, the above-mentioned parameters and the results of the simulation (residual velocity and residual mass of the projectile) are used as input and output data to train and validate a neural network. In addition, the number of training cases needed to arrive at a given predictive error is studied. The results are satisfactory, this alternative providing a highly recommended option for armour design tasks, due to its simplicity of handling, low computational cost and efficiency.This research was done with the financial support of the Comunidad Autónoma de Madrid under Project GR/MAT/0507/2004.Publicad