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

Deep Belief Network Training Improvement Using Elite Samples Minimizing Free Energy

Nowadays this is very popular to use deep architectures in machine learning. Deep Belief Networks (DBNs) are deep architectures that use stack of Restricted Boltzmann Machines (RBM) to create a powerful generative model using training data. In this paper we present an improvement in a common method that is usually used in training of RBMs. The new method uses free energy as a criterion to obtain elite samples from generative model. We argue that these samples can more accurately compute gradient of log probability of training data. According to the results, an error rate of 0.99% was achieved on MNIST test set. This result shows that the proposed method outperforms the method presented in the first paper introducing DBN (1.25% error rate) and general classification methods such as SVM (1.4% error rate) and KNN (with 1.6% error rate). In another test using ISOLET dataset, letter classification error dropped to 3.59% compared to 5.59% error rate achieved in those papers using this dataset. The implemented method is available online at "http://ceit.aut.ac.ir/~keyvanrad/DeeBNet Toolbox.html".Comment: 18 pages. arXiv admin note: substantial text overlap with arXiv:1408.326