Quantum-state transfer in imperfect artificial spin networks

High-fidelity quantum computation and quantum state transfer are possible in short spin chains. We exploit a system based on a dispersive qubit-boson interaction to mimic XY coupling. In this model, the usually assumed nearest-neighbor coupling is no longer valid: all the qubits are mutually coupled. We analyze the performances of our model for quantum state transfer showing how preengineered coupling rates allow for nearly optimal state transfer. We address a setup of superconducting qubits coupled to a microstrip cavity in which our analysis may be applied.

Modelling the correlation between processing parameters and properties of maraging steels using artificial neural network

An artificial neural network (ANN) model is developed for the analysis and simulation of the correlation between the properties of maraging steels and composition, processing and working conditions. The input parameters of the model consist of alloy composition, processing parameters (including cold deformation degree, ageing temperature, and ageing time), and working temperature. The outputs of the ANN model include property parameters namely: ultimate tensile strength, yield strength, elongation, reduction in area, hardness, notched tensile strength, Charpy impact energy, fracture toughness, and martensitic transformation start temperature. Good performance of the ANN model is achieved. The model can be used to calculate properties of maraging steels as functions of alloy composition, processing parameters, and working condition. The combined influence of Co and Mo on the properties of maraging steels is simulated using the model. The results are in agreement with experimental data. Explanation of the calculated results from the metallurgical point of view is attempted. The model can be used as a guide for further alloy development.

Artificial Intelligence and Cognitive Science

Editorial for 17th AICS Conference

Uniplanar left-handed artificial metamaterials

Planar periodic arrays of metallic elements printed on grounded dielectric substrates are presented to exhibit left-handed properties for surface wave propagation. The proposed structures dispense with the need for grounding vias and ease the implementation of uniplanar left-handed metamaterials at higher frequencies. A transmission line description is used for the initial design and interpretation of the left-handed property. A thorough study based on full wave simulations is carried out with regards to the effect of the element geometrical characteristics and the array periodicity to the properties of the artificial material. Dispersion curves are presented and studied. The distribution of the modal fields in the unit cell is also studied in order to provide an explanation of the material properties. The scalability of the proposed structures to infrared frequencies is demonstrated.

Artificial magnetic conductor surfaces and their application to low-profile high-gain planar antennas

Planar periodic metallic arrays behave as artificial magnetic conductor (AMC) surfaces when placed on a grounded dielectric substrate and they introduce a zero degrees reflection phase shift to incident waves. In this paper the AMC operation of single-layer arrays without vias is studied using a resonant cavity model and a new application to high-gain printed antennas is presented. A ray analysis is employed in order to give physical insight into the performance of AMCs and derive design guidelines. The bandwidth and center frequency of AMC surfaces are investigated using full-wave analysis and the qualitative predictions of the ray model are validated. Planar AMC surfaces are used for the first time as the ground plane in a high-gain microstrip patch antenna with a partially reflective surface as superstrate. A significant reduction of the antenna profile is achieved. A ray theory approach is employed in order to describe the functioning of the antenna and to predict the existence of quarter wavelength resonant cavities.