An universal quantum computation scheme with low error diffusion property

Quantum concatenation code is an effective way to realize fault-tolerant universal quantum computing. Still, there are many non-fault-tolerant logical locations at its low encoding level, which thereby increases the probability of error multiplication and limits the ability that such code to realize a high-fidelity universal gate library. In this work, we propose a general framework based on machine learning technology for the decoder design of a segmented fault-tolerant quantum circuit. Then following this design principle, we adopt the neural network algorithm to give an optimized decoder for the such circuit. To assess the effectiveness of our new decoder, we apply it to the segmented fault-tolerant logical controlled-NOT gates, which act on the tensor composed of the Steane 7-qubit logical qubit and the Reed-Muller 15-qubit logical qubit. We simulate these gates under depolarizing noise environment and compare the gate error thresholds in contrast to the minimal-weight decoder. Finally, we provide a fault-tolerant universal gate library based on a 33-qubit non-uniform concatenated code. Furthermore, we offer several level-1 segmented fault-tolerant locations with optimized decoders to construct a non-Clifford gate on this code, which has less circuit depth than our existing work. Meanwhile, we analyze the pseudo-threshold of the universal scheme of this code.Comment: 21 pages,13 figure

Reciprocal polarization imaging of complex media

The vectorial evolution of polarized light interaction with a medium can reveal its microstructure and anisotropy beyond what can be obtained from scalar light interaction. Anisotropic properties (diattenuation, retardance, and depolarization) of a complex medium can be quantified by polarization imaging by measuring the Mueller matrix. However, polarization imaging in the reflection geometry, ubiquitous and often preferred in diverse applications, has suffered a poor recovery of the medium's anisotropic properties due to the lack of suitable decomposition of the Mueller matrices measured inside a backward geometry. Here, we present reciprocal polarization imaging of complex media after introducing reciprocal polar decomposition for backscattering Mueller matrices. Based on the reciprocity of the optical wave in its forward and backward scattering paths, the anisotropic diattenuation, retardance, and depolarization of a complex medium are determined by measuring the backscattering Mueller matrix. We demonstrate reciprocal polarization imaging in various applications for quantifying complex non-chiral and chiral media (birefringence resolution target, tissue sections, and glucose suspension), uncovering their anisotropic microstructures with remarkable clarity and accuracy. We also highlight types of complex media that Lu-Chipman and differential decompositions of backscattering Mueller matrices lead to erroneous medium polarization properties, whereas reciprocal polar decomposition recovers properly. Reciprocal polarization imaging will be instrumental in imaging complex media from remote sensing to biomedicine and will open new applications of polarization optics in reflection geometry

A Facile Synthesis of 3-Substituted 9H-Pyrido[3,4-b]indol- 1(2H)-one Derivatives from 3-Substituted β-Carbolines

molecule

The Time-Scaling and Pitch-Shifting Algorithms for Blackfin DSP

Conference Name:IEEE International Symposium on IT in Medicine and Education. Conference Address: Xiamen, PEOPLES R CHINA. Time:DEC 12-14, 2008.Audio effects processing technology is widely used in various types of music production, live performances and other occasions. Most of traditional technologies produce real-time audio effects with hardware analog circuits. In this paper, focus on the demand of time scaling, pitch shifting and special effects of real-time audio. Improve the algorithm performance, efficiency and effectiveness in subjective base on traditional phase vocoder [1] model in high performance DSP processor BlackFin which be made the use of its structural features. Experiment shows that, new algorithm do well in both computation and hearing effect

Weakly-supervised cross-domain road scene segmentation via multi-level curriculum adaptation

Semantic segmentation, which aims to acquire pixel-level understanding about images, is among the key components in computer vision. To train a good segmentation model for real-world images, it usually requires a huge amount of time and labor effort to obtain sufficient pixel-level annotations of real-world images beforehand. To get rid of such a nontrivial burden, one can use simulators to automatically generate synthetic images that inherently contain full pixel-level annotations and use them to train a segmentation model for the real-world images. However, training with synthetic images usually cannot lead to good performance due to the domain difference between the synthetic images (i.e., source domain) and the real-world images (i.e., target domain). To deal with this issue, a number of unsupervised domain adaptation (UDA) approaches have been proposed, where no labeled real-world images are available. Different from those methods, in this work, we conduct a pioneer attempt by using easy-to-collect image-level annotations for target images to improve the performance of cross-domain segmentation. Specifically, we leverage those image-level annotations to construct curriculums for the domain adaptation problem. The curriculums describe multi-level properties of the target domain, including label distributions over full images, local regions and single pixels. Since image annotations are 'weak' labels compared to pixel annotations for segmentation, we coin this new problem as weakly-supervised cross-domain segmentation. Comprehensive experiments on the GTA5 -> Cityscapes and SYNTHIA -> Cityscapes settings demonstrate the effectiveness of our method over the existing state-of-the-art baselines.This work was supported in part by the Major Project for New Generation of AI under Grant 2018AAA0100400; in part by the National Natural Science Foundation of China under Grant 11829101, Grant 11931014, and Grant 61772118; and in part by the Fundamental Research Funds for the Central Universities of China under Grant JBK1806002

Effective Recovery of Vanadium from Oil Refinery Waste into Vanadium-Based Metal–Organic Frameworks

10.1021/acs.est.7b04989Environmental Science & Technology523008-301

Synthesis of gold nanoparticles by Cacumen Platycladi leaf extract and its simulated solution: toward the plant-mediated biosynthetic mechanism

In this study, biogenic fabrication of gold nanoparticles (AuNPs), respectively, by Cacumen Platycladi leaf extract and the simulation of its active components were thoroughly investigated. The simulated solution was prepared based on components measurement and Fourier-transform infrared spectroscopy analysis of Cacumen Platycladi leaf extract before and after reaction. Several analytic methods such as UV-Vis spectrophotometry, X-ray diffraction, transmission electron microscopy, and thermogravimetric study were adopted to characterize the AuNPs. The results showed that flavonoid and reducing sugar were the main reductive and protective components in the extract vital in the biosynthesis of the AuNPs. In addition, pH of the reaction solution was proved to be the most significant factor upon the synthesis process. The bioreduction mechanism of chloroaurate ions and the formation mechanism of AuNPs were also discussed. To the best of our knowledge, this is the first report on plausible elucidation of the biosynthetic mechanism through comparative study between a plant extract and its simulated solution.National Natural Science Foundation of China[21036004, 20776120, 20976146, 30700020]; Natural Science Foundation of Fujian Province of China[2010J05032, 2010J01052]; Fundamental Research Funds for the Central Universities[2010121051