A significant obstacle for practical quantum computation is the loss of physical qubits in quantum computers, a decoherence mechanism most notably in optical systems. Here we experimentally demonstrate, both in the quantum circuit model and in the on
e-way quantum computer model, the smallest non-trivial quantum codes to tackle this problem. In the experiment, we encode single-qubit input states into highly-entangled multiparticle codewords, and we test their ability to protect encoded quantum information from detected one-qubit loss error. Our results prove the in-principle feasibility of overcoming the qubit loss error by quantum codes.
Extensive investigations show that QED$_{3}$ exhibits dynamical fermion mass generation at zero temperature when the fermion flavor $N$ is sufficiently small. However, it seems difficult to extend the theoretical analysis to finite temperature. We st
udy this problem by means of Dyson-Schwinger equation approach after considering the effect of finite temperature or disorder-induced fermion damping. Under the widely used instantaneous approximation, the dynamical mass displays an infrared divergence in both cases. We then adopt a new approximation that includes an energy-dependent gauge boson propagator and obtain results for dynamical fermion mass that do not contain infrared divergence. The validity of the new approximation is examined by comparing to the well-established results obtained at zero temperature.
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to deal with multimodal data, such as in image annotation tasks. Another popular approach to model the multimodal data is through deep neural networks, such as t
he deep Boltzmann machine (DBM). Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was proposed and demonstrated state-of-the-art performance for text document modeling. In this work, we show how to successfully apply and extend this model to multimodal data, such as simultaneous image classification and annotation. First, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the learned hidden topic features and show how to employ it to learn a joint representation from image visual words, annotation words and class label information. We test our model on the LabelMe and UIUC-Sports data sets and show that it compares favorably to other topic models. Second, we propose a deep extension of our model and provide an efficient way of training the deep model. Experimental results show that our deep model outperforms its shallow version and reaches state-of-the-art performance on the Multimedia Information Retrieval (MIR) Flickr data set.
We fit the spectral energy distributions (SEDs) of a GeV-TeV FSRQ sample with the leptonic model. Their gamma_min of the relativistic electron distributions, which significantly affect the estimates of the jet properties, are constrained, with a typi
cal value of 48. Their jet power, magnetized parameter, radiation efficiency, and jet production/radiation rates per central black hole (BH) mass are derived and compared to that of BL Lacs. We show that he FSRQ jets may be dominated by the Poynting flux and have a high radiation efficiency, whereas the BL Lac jets are likely dominated by particles and have a lower radiation efficiency than FSRQs. Being different from BL Lacs, the jet powers of FSRQs are proportional to their central BH masses. The jet production and radiation rates of the FSRQs distribute in narrow ranges and are correlated with each other, whereas no similar feature is found for the BL Lacs. We also show that the jet power is correlated with the cavity kinetic power, and the magnetic field energy in the jets may provide the cavity kinetic energy of FSRQs and the kinetic energy of cold protons in the jets may be crucial for cavity kinetic energy of BL Lacs. We suggest that the dominating formation mechanism of FSRQ jets may be the BZ process, but BL Lac jets may be produced via the BP and/or BZ processes, depending on the structures and accretion rates of accretion disks.
Gamma-ray bursts (GRBs) and GeV-TeV selected radio loud Active Galactic Nuclei (AGNs) are compared based on our systematic modeling of the observed spectral energy distributions of a sample of AGNs with a single-zone leptonic model. We show that the
correlation between the jet power (P_{jet}) and the prompt gamma-ray luminosity (L_{jet}) of GRBs is consistent, within the uncertainties, with the correlation between jet power and the synchrotron peak luminosity (L_{s, jet}) of flat spectrum radio quasars (FSRQs). Their radiation efficiencies (varepsilon) are also comparable (>10% for most sources), which increase with the bolometric jet luminosity (L_{bol,jet}) for FSRQs and with the L_{jet} for GRBs with similar power-law indices. BL Lacs do not follow the P_{jet}-L_{s, jet} relation of FSRQs. They have lower varepsilon and L_{bol, jet} values than FSRQs, and a tentative L_{bol, jet}-varepsilon relation is also found, with a power-law index being different from that of the FSRQs. The magnetization parameters (sigma) of FSRQs are averagely larger than that of BL Lacs. They are anti-correlated with $varepsilon$ for the FSRQs, but positive correlated with varepsilon for the BL Lacs. GeV Narrow-line Seyfert 1 galaxies potentially share similar properties with FSRQs. Based on the analogy between GRBs and FSRQs, we suggest that the prompt gamma-ray emission of GRBs is likely produced by synchrotron process in a magnetized jet with high radiation efficiency, similar to FSRQs. The jets of BL Lacs, on the other hand, are less efficient and are likely more matter dominated.
Topic modeling based on latent Dirichlet allocation (LDA) has been a framework of choice to perform scene recognition and annotation. Recently, a new type of topic model called the Document Neural Autoregressive Distribution Estimator (DocNADE) was p
roposed and demonstrated state-of-the-art performance for document modeling. In this work, we show how to successfully apply and extend this model to the context of visual scene modeling. Specifically, we propose SupDocNADE, a supervised extension of DocNADE, that increases the discriminative power of the hidden topic features by incorporating label information into the training objective of the model. We also describe how to leverage information about the spatial position of the visual words and how to embed additional image annotations, so as to simultaneously perform image classification and annotation. We test our model on the Scene15, LabelMe and UIUC-Sports datasets and show that it compares favorably to other topic models such as the supervised variant of LDA.
Stacked layers of metal meshes embedded in a dielectric substrate are routinely used for providing spectral selection at THz frequencies. Recent work has shown that particular geometries allow the refractive index to be tuned to produce practical art
ificial materials. Here we show that by spatially grading in the plane of the mesh we can manufacture a Graded Index (GrIn) thin flat lens optimized for use at THz frequencies. Measurements on a prototype lens show we are able to obtain the parabolic profile of a Woods type lens which is dependent only on the mesh parameters. This technique could realize other exotic optical devices.
