Dynamical behavior of damped driven coupled single electron simple harmonic oscillators

Coherent coupling between a large number of qubits is the goal for scalable approaches to solid state quantum information processing. Prototype systems can be characterized by spectroscopic techniques. Here, we use pulsed-continuous wave microwave spectroscopy to study the behavior of electrons trapped at defects within the gate dielectric of a sol-gel-based high-k silicon MOSFET. Disorder leads to a wide distribution in trap properties, allowing more than 1000 traps to be individually addressed in a single transistor within the accessible frequency domain. Their dynamical behavior is explored by pulsing the microwave excitation over a range of times comparable to the phase coherence time and the lifetime of the electron in the trap. Trap occupancy is limited to a single electron, which can be manipulated by resonant microwave excitation and the resulting change in trap occupancy is detected by the change in the channel current of the transistor. The trap behavior is described by a classical damped driven simple harmonic oscillator model, with the phase coherence, lifetime and coupling strength parameters derived from a continuous wave (CW) measurement only. For pulse times shorter than the phase coherence time, the energy exchange between traps, due to the coupling, strongly modulates the observed drain current change. This effect could be exploited for 2-qubit gate operation. The very large number of resonances observed in this system would allow a complex multi-qubit quantum mechanical circuit to be realized by this mechanism using only a single transistor.

Some examples of multiple solutions in limiting flaw calculations using PD6493:1991 fracture mechanics analyses

The use of PC-based PD6493:1991 fracture assessment procedures has revealed that, under certain circumstances, flaws of different dimensions may be found as being limiting or critical for identical applied conditions. The main causes for multiple solutions are a steep applied stress gradient, residual stress relaxation and flaw re-characterisation. This work uses several case studies to illustrate some of the circumstances under which multiple solutions occurs.

Nanostructured carbon films from supersonic cluster beam deposition: Structure and morphology

Nanostructured carbon thin films have been grown by deposition of cluster beams produced by a supersonic expansion. Due to separation effects typical of supersonic beams, films with different nanostructures can be grown by the simple intercepting of different regions of the cluster beam with a substrate. Films show a low-density porous structure, which has been characterized by Raman and Brillouin spectroscopy. Film morphology suggests that growth processes are similar to those occurring in a ballistic deposition regime.

Numerical analysis of the coupled circuit and cooling holes for an electromagnetic shaker

This paper presents a time-stepping shaker modeling scheme. The new method improves the accuracy of analysis of armature-position-dependent inductances and force factors, analysis of axial variation of current density in copper plates (short-circuited turns), and analysis of cooling holes in the magnetic circuit. Linear movement modeling allows armature position to be precisely included in the shaker analysis. A more accurate calculation of eddy currents in the coupled circuit is in particular crucial for the shaker analysis in a mid-or high-frequency operation range. Large currents in a shaker, including eddy currents, incur large Joule losses, which in turn require the use of a cooling system to keep temperature at bay. Sizable cooling holes have influence on the saturation state of iron poles, and hence have to be properly taken into account.

Cluster analysis of heterogeneous rank data

Cluster analysis of ranking data, which occurs in consumer questionnaires, voting forms or other inquiries of preferences, attempts to identify typical groups of rank choices. Empirically measured rankings are often incomplete, i.e. different numbers of filled rank positions cause heterogeneity in the data. We propose a mixture approach for clustering of heterogeneous rank data. Rankings of different lengths can be described and compared by means of a single probabilistic model. A maximum entropy approach avoids hidden assumptions about missing rank positions. Parameter estimators and an efficient EM algorithm for unsupervised inference are derived for the ranking mixture model. Experiments on both synthetic data and real-world data demonstrate significantly improved parameter estimates on heterogeneous data when the incomplete rankings are included in the inference process.