Attainable entanglement of unitary transformed thermal states in liquid-state nuclear magnetic resonance with the chemical shift

Recently, Yu, Brown, and Chuang [Phys. Rev. A {\bf 71}, 032341 (2005)] investigated the entanglement attainable from unitary transformed thermal states in liquid-state nuclear magnetic resonance (NMR). Their research gave an insight into the role of the entanglement in a liquid-state NMR quantum computer. Moreover, they attempted to reveal the role of mixed-state entanglement in quantum computing. However, they assumed that the Zeeman energy of each nuclear spin which corresponds to a qubit takes a common value for all; there is no chemical shift. In this paper, we research a model with the chemical shifts and analytically derive the physical parameter region where unitary transformed thermal states are entangled, by the positive partial transposition (PPT) criterion with respect to any bipartition. We examine the effect of the chemical shifts on the boundary between the separability and the nonseparability, and find it is negligible.Comment: 9 pages, 1 figures. There were mistakes in the previous version. The main results don't change, but our motivation has to be reconsidere

Single-epoch supernova classification with deep convolutional neural networks

Supernovae Type-Ia (SNeIa) play a significant role in exploring the history of the expansion of the Universe, since they are the best-known standard candles with which we can accurately measure the distance to the objects. Finding large samples of SNeIa and investigating their detailed characteristics have become an important issue in cosmology and astronomy. Existing methods relied on a photometric approach that first measures the luminance of supernova candidates precisely and then fits the results to a parametric function of temporal changes in luminance. However, it inevitably requires multi-epoch observations and complex luminance measurements. In this work, we present a novel method for classifying SNeIa simply from single-epoch observation images without any complex measurements, by effectively integrating the state-of-the-art computer vision methodology into the standard photometric approach. Our method first builds a convolutional neural network for estimating the luminance of supernovae from telescope images, and then constructs another neural network for the classification, where the estimated luminance and observation dates are used as features for classification. Both of the neural networks are integrated into a single deep neural network to classify SNeIa directly from observation images. Experimental results show the effectiveness of the proposed method and reveal classification performance comparable to existing photometric methods with multi-epoch observations.Comment: 7 pages, published as a workshop paper in ICDCS2017, in June 201

Facile synthetic procedure for and electrochemical properties of hexa(2-thienyl)benzenes directed toward electroactive materials

In the presence of RhCl3 center dot 3H(2)O and i-Pr2NEt, the cyclotrimerization of di(2-thienyl)acetylenes proceeded smoothly to afford hexa(2-thienyl)benzenes. CV analysis of the hexa(2-thienyl)benzenes showed that they may be useful as electroactive materials.</p

Some consequences of the A/A-bar distinction of scrambling

It has been discussed that clause intermal scrambling (CIS) involves A or A-bar movement whereas long distance scrambling (LDS) involves A-bar movement (cf. Mahajan (1989), Webelhuth (1989), Saito (1992)). ..