Shot noise in charge and magnetization currents of a quantum ring

The shot noise in a quantum ring, connected to leads, is studied in the presence of electron interactions in the sequential tunneling regime. Two qualitatively different noise correlations with distinctly different behaviors are identified and studied in a large range of parameters. Noise in the total current is due to the discreteness of the electron charge and can become super-Poissonian as result of electron interaction. The noise in the magnetization current is comparatively insensitive to the interaction but can be greatly enhanced if population inversion of the angular states is assumed. The characteristic time scales are studied by a Monte-Carlo simulation.Comment: 5 pages, 5 color figure

Fermion-parity duality and energy relaxation in interacting open systems

We study the transient heat current out of a confined electron system into a weakly coupled electrode in response to a voltage switch. We show that the decay of the Coulomb interaction energy for this repulsive system exhibits signatures of electron-electron attraction, and is governed by an interaction-independent rate. This can only be understood from a general duality that relates the non-unitary evolution of a quantum system to that of a dual model with inverted energies. Deriving from the fermion-parity superselection postulate, this duality applies to a large class of open systems.Comment: 5 pages + 19 pages of Supplementary Materia

Transfer of a quantum state from a photonic qubit to a gate-defined quantum dot

Interconnecting well-functioning, scalable stationary qubits and photonic qubits could substantially advance quantum communication applications and serve to link future quantum processors. Here, we present two protocols for transferring the state of a photonic qubit to a single-spin and to a two-spin qubit hosted in gate-defined quantum dots (GDQD). Both protocols are based on using a localized exciton as intermediary between the photonic and the spin qubit. We use effective Hamiltonian models to describe the hybrid systems formed by the the exciton and the GDQDs and apply simple but realistic noise models to analyze the viability of the proposed protocols. Using realistic parameters, we find that the protocols can be completed with a success probability ranging between 85-97%

Improved bounds for sparse recovery from adaptive measurements

It is shown here that adaptivity in sampling results in dramatic improvements in the recovery of sparse signals in white Gaussian noise. An adaptive sampling-and-refinement procedure called distilled sensing is discussed and analyzed, resulting in fundamental new asymptotic scaling relationships in terms of the minimum feature strength required for reliable signal detection or localization (support recovery). In particular, reliable detection and localization using non-adaptive samples is possible only if the feature strength grows logarithmically in the problem dimension. Here it is shown that using adaptive sampling, reliable detection is possible provided the feature strength exceeds a constant, and localization is possible when the feature strength exceeds any (arbitrarily slowly) growing function of the problem dimension

Hierarchical Strategy of Model Partitioning for VLSI-Design Using an Improved Mixture of Experts Approach

The partitioning of complex processor models on the gate and register-transfer level for parallel functional simulation based on the clock-cycle algorithm is considered. We introduce a hierarchical partitioning scheme combining various partitioning algorithms in the frame of a competing strategy. Melting together different partitioning results within one level using superpositions we crossover to a mixture of experts one. This approach is improved applying genetic algorithms. In addition we present two new partitioning algorithms both of them taking cones as fundamental units for building partitions

The zero effect: voxel-based lesion symptom mapping of number transcoding errors following stroke

Zero represents a special case in our numerical system because it is not represented on a semantic level. Former research has shown that this can lead to specific impairments when transcoding numerals from dictation to written digits. Even though, number processing is considered to be dominated by the left hemisphere, studies have indicated that both left as well as right hemispheric stroke patients commit errors when transcoding numerals including zeros. Here, for the first time, a large sample of subacute stroke patients (N = 667) was assessed without being preselected based on the location of their lesion, or a specific impairment in transcoding zero. The results show that specific errors in transcoding zeros were common (prevalence = 14.2%) and a voxel-based lesion symptom mapping analysis (n = 153) revealed these to be related to lesions in and around the right putamen. In line with former research, the present study argues that the widespread brain network for number processing also includes subcortical regions, like the putamen with connections to the insular cortex. These play a crucial role in auditory perception as well as attention. If these areas are lesioned, number processing tasks with higher attentional and working memory loads, like transcoding zeros, can be impaired

Periodic Pattern Detection for Real-Time Application

Abstract. Digital video stabilization approaches typically degrade their performances in presence of periodic patterns. Any kind of matching between consecutive frames is not usually able to work in presence of these kind of signals: the motion estimation engine is deceived and its performances degrade abruptly. In this paper we propose a fast fuzzy classifier able to recognize periodic and aperiodic pattern in the images that takes into account the peculiarities of digital video stabilization. Finally, the proposed classifier can be used as a filtering module in a block based video stabilization approach. Key words: Video Stabilization, periodic pattern, fuzzy classifier

Hierarchical Model Partitioning for Parallel VLSI-Simulation Using Evolutionary Algorithms improved bei superpositions of partitions

Parallelization of VLSI-simulation exploiting model-inherent parallelism is a promising way to accelerate verification processes for whole processor designs. Thereby partitioning of hardware models influences the effciency of following parallel simulations essentially. Based on a formal model of Parallel Cycle Simulation we introduce partition valuation combining communication and load balancing aspects. We choose a 2-level hierarchical partitioning scheme providing a framework for a mixture of experts strategy. Considering a complete model of a PowerPC 604 processor, we demonstrate that Evolutionary Algorithms can be applied successfully to our model partitioning problem on the second hierarchy level, supposing a reduced problem complexity after fast pre-partitioning on the first level. For the first time, we apply superpositions during execution of Evolutionary Algorithms, resulting in a faster decreasing fitness function and an acceleration of population handling

CNOT and Bell-state analysis in the weak-coupling cavity QED regime

We propose an interface between the spin of a photon and the spin of an electron confined in a quantum dot embedded in a microcavity operating in the weak coupling regime. This interface, based on spin selective photon reflection from the cavity, can be used to construct a CNOT gate, a multi-photon entangler and a photonic Bell-state analyzer. Finally, we analyze experimental feasibility, concluding that the schemes can be implemented with current technology.Comment: 4 pages, 2 figure

Finding needles in noisy haystacks

The theory of compressed sensing shows that samples in the form of random projections are optimal for recovering sparse signals in high-dimensional spaces (i.e., finding needles in haystacks), provided the measurements are noiseless. However, noise is almost always present in applications, and compressed sensing suffers from it. The signal to noise ratio per dimension using random projections is very poor, since sensing energy is equally distributed over all dimensions. Consequently, the ability of compressed sensing to locate sparse components degrades significantly as noise increases. It is possible, in principle, to improve performance by "shaping" the projections to focus sensing energy in proper dimensions. The main question addressed here is, can projections be adaptively shaped to achieve this focusing effect? The answer is yes, and we demonstrate a simple, computationally efficient procedure that does so