Mechanically induced spin resonance in a carbon nanotube

The electron spin is a promising qubit candidate for quantum computation and quantum information. Here we propose and analyze a mechanically-induced single electron spin resonance, which amounts to a rotation of the spin about the $x$-axis in a suspended carbon nanotube. The effect is based on the coupling between the spin and the mechanical degree of freedom due to the intrinsic curvature-induced spin-orbit coupling. A rotation about the $z$-axis is obtained by the off-resonant external electric driving field. Arbitrary-angle rotations of the single electron spin about any axis in the $x$-$z$ plane can be obtained with a single operation by varying the frequency and the strength of the external electric driving field. With multiple steps combining the rotations about the $x$- and $z$-axes, arbitrary-angle rotations about arbitrary axes can be constructed, which implies that any single-qubit gate of the electron spin qubit can be performed. We simulate the system numerically using a master equation with realistic parameters.Comment: 7 pages, 4 figure

Monogamy of Einstein-Podolsky-Rosen steering in the background of an asymptotically flat black hole

We study the behavior of monogamy deficit and monogamy asymmetry for Einstein-Podolsky-Rosen steering of Gaussian states under the influence of the Hawking effect. We demonstrate that the monogamy of quantum steering shows an extreme scenario in the curved spacetime: the first part of a tripartite system cannot individually steer two other parties, but it can steer the collectivity of the remaining two parties. We also find that the monogamy deficit of Gaussian steering, a quantifier of genuine tripartite steering, are generated due to the influence of the Hawking thermal bath. Our results elucidate the structure of quantum steering in tripartite quantum systems in curved spacetime.Comment: 16 pages, 4 figure

Text Generation Based on Generative Adversarial Nets with Latent Variable

In this paper, we propose a model using generative adversarial net (GAN) to generate realistic text. Instead of using standard GAN, we combine variational autoencoder (VAE) with generative adversarial net. The use of high-level latent random variables is helpful to learn the data distribution and solve the problem that generative adversarial net always emits the similar data. We propose the VGAN model where the generative model is composed of recurrent neural network and VAE. The discriminative model is a convolutional neural network. We train the model via policy gradient. We apply the proposed model to the task of text generation and compare it to other recent neural network based models, such as recurrent neural network language model and SeqGAN. We evaluate the performance of the model by calculating negative log-likelihood and the BLEU score. We conduct experiments on three benchmark datasets, and results show that our model outperforms other previous models