Anti-symmetry consideration on the preservation of Entanglement of spin system

In this work we offer an approach to protect the entanglement based on the anti-symmetric property of the hamiltonian. Our main objective is to protect the entanglement of a given initial three-qubit state which is governed by hamiltonian of a three-spin Ising chain in site-dependent transverse fields. We show that according to anti-symmetric property of the hamiltonian with respect to some operators mimicking the time reversal operator, the dynamics of the system can be effectively reversed. It equips us to control the dynamics of the system. The control procedure is implemented as a sequence of cyclic evolution; accordingly the entanglement of the system is protected for any given initial state with any desired accuracy an long-time. Using this approach we could control not only the multiparty entanglement but also the pairwise entanglement. It is also notable that in this paper although we restrict ourselves mostly within a three-spin Ising chain in site-dependent transverse fields, our approach could be applicable to any n-qubit spin system models.Comment: 13 pages, 4 figure

Top-gating of p-Si/SiGe/Si inverted modulation-doped structures

Low-temperature electrical properties of two-dimensional hole gases (2-DHGs) in Si/Si0.8Ge0.2/Si inverted modulation-doped structures have been investigated at different hole densities using a metal semiconductor gate sputtered on top of these structures. The 2-DHG which is supplied to the inverted interface of Si/SiGe/Si quantum well by a Si boron-doped layer spatially grown beneath the alloy, was controlled in the range of 1.5–7.8×1011 cm–2 hole density by biasing the top gate. With increasing 2-DHG sheet density, the hole wave function of these structures expands and moves away from inverted interface, consequently the mobility enhances. These results may be understood theoretically by elaborating the role of interface charge, roughness, and alloy scattering mechanisms in limiting the mobility of holes at the inverted interface

Bayesian inference for inverse problems

Traditionally, the MaxEnt workshops start by a tutorial day. This paper summarizes my talk during 2001'th workshop at John Hopkins University. The main idea in this talk is to show how the Bayesian inference can naturally give us all the necessary tools we need to solve real inverse problems: starting by simple inversion where we assume to know exactly the forward model and all the input model parameters up to more realistic advanced problems of myopic or blind inversion where we may be uncertain about the forward model and we may have noisy data. Starting by an introduction to inverse problems through a few examples and explaining their ill posedness nature, I briefly presented the main classical deterministic methods such as data matching and classical regularization methods to show their limitations. I then presented the main classical probabilistic methods based on likelihood, information theory and maximum entropy and the Bayesian inference framework for such problems. I show that the Bayesian framework, not only generalizes all these methods, but also gives us natural tools, for example, for inferring the uncertainty of the computed solutions, for the estimation of the hyperparameters or for handling myopic or blind inversion problems. Finally, through a deconvolution problem example, I presented a few state of the art methods based on Bayesian inference particularly designed for some of the mass spectrometry data processing problems.Comment: Presented at MaxEnt01. To appear in Bayesian Inference and Maximum Entropy Methods, B. Fry (Ed.), AIP Proceedings. 20pages, 13 Postscript figure

Yet Another Analysis of Dice Problems

During the MaxEnt 2002 workshop in Moscow, Idaho, Tony Vignaux asked again a few simple questions about using Maximum Entropy or Bayesian approaches for the famous Dice problems which have been analyzed many times through this workshop and also in other places. Here, there is another analysis of these problems. I hope that, this paper will answer a few questions of Tony and other participants of the workshop on the situations where we can use Maximum Entropy or Bayesian approaches or even the cases where we can actually use both of them. Keywords: Dice problems and probability theory, Maximum Likelihood, Bayesian inference, Maximum A Posteriori, Entropy, Maximum entropy, Maximum entropy in the mean.Comment: Presented at MaxEnt2002, the 22nd International Workshop on Bayesian and Maximum Entropy methods (Aug. 3-9, 2002, Moscow, Idaho, USA). To appear in Proceedings of American Institute of Physic