6 resultados para NMR quantum computing

em Aston University Research Archive


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Miniature slow light Surface Nanoscale Axial Photonics (SNAP) devices are reviewed. The fabrication precision of these devices is two orders of magnitude higher and the transmission losses are two orders of magnitude smaller than for any of the previously reported technologies for fabrication of miniature photonic circuits. In the first part of the report, a SNAP bottle resonator with a few nm high radius variation is demonstrated as the record small, slow light, and low loss 2.6 ns dispersionless delay line of 100 ps pulses. Next, a record small SNAP bottle resonator exhibiting the 20 ns/nm dispersion compensation of 100 ps pulses is demonstrated. In the second part of the report, the prospects of the SNAP technology in applications to telecommunications, optical signal processing, quantum computing, and microfluidics are discussed. © 2014 IEEE.

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Grafted GMA on EPR samples were prepared in a Thermo-Haake internal mixer by free radical melt grafting reactions in the absence (conventional system; EPR-g-GMA(CONV)) and presence of the reactive comonomer divinyl benzene, DVB (EPR-g-GMA(DVB)). The GMA-homopolymer (poly-GMA), a major side reaction product in the conventional system, was almost completely absent in the DVB-containing system, the latter also resulted in a much higher level of GMA grafting. A comprehensive microstructure analysis of the formed poly-GMA was performed based on one-dimensional H-1 and C-13 NMR spectroscopy and the complete spectral assignments were supported by two-dimensional NMR techniques based on long range two and three bond order carbon-proton couplings from HMBC (Heteronuclear Multiple Bond Coherence) and that of one bond carbon-proton couplings from HSQC (Heteronuclear Single Quantum Coherence), as well as the use of Distortionless Enhancement by Polarization Transfer (DEPT) NMR spectroscopy. The unambiguous analysis of the stereochemical configuration of poly-GMA was further used to help understand the microstructures of the GMA-grafts obtained in the two different free radical melt grafting reactions, the conventional and comonomer-containing systems. In the grafted GMA, in the conventional system (EPR-g-GMA(CONV)), the methylene protons of the GMA were found to be sensitive to tetrad configurational sequences and the results showed that 56% of the GMA sequence in the graft is in atactic configuration and 42% is in syndiotactic configuration whereas the poly-GMA was predominantly syndiotactic. The differences in the microstructures of the graft in the conventional EPR-g-GMA(CONV) and the DVB-containing (EPR-g-GMA(DVB)) systems is also reported (C) 2009 Elsevier Ltd. All rights reserved.

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The atomic-scale structure of Bioglass and the effect of substituting lithium for sodium within these glasses have been investigated using neutron diffraction and solid state magic angle spinning (MAS) NMR. Applying an effective isomorphic substitution difference function to the neutron diffraction data has enabled the Na-O and Li-O nearest-neighbour correlations to be isolated from the overlapping Ca-O, O-(P)-O and O-(Si)-O correlations. These results reveal that Na and Li behave in a similar manner within the glassy matrix and do not disrupt the short range order of the network former. Residual differences are attributed solely to the variation in ionic radius between the two species. Successful simplification of the 2 quantum MAS (3QMAS) NMR data corroborates the split Na-O correlations. The structural sites present will be intimately related to the release properties of the glass system in physiological fluids such as plasma and saliva, and hence to the bioactivity of the material. Detailed structural knowledge is therefore a prerequisite for optimizing material design.

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Strontium has been substituted for calcium in the glass series (SiO2)49.46(Na2O)26.38(P2O5)1.07(CaO)23.08x(SrO)x (where x = 0, 11.54, 23.08) to elucidate their underlying atomic-scale structural characteristics as a basis for understanding features related to the bioactivity. These bioactive glasses have been investigated using isomorphic neutron and X-ray diffraction, Sr K-edge EXAFS and solid state 17O, 23Na, 29Si, 31P and 43Ca magic-angle-spinning (MAS) NMR. An effective isomorphic substitution first-order difference function has been applied to the neutron diffraction data, confirming that Ca and Sr behave in a similar manner within the glass network, with residual differences attributed to solely the variation in ionic radius between the two species. The diffraction data provides the first direct experimental evidence of split Ca–O nearest-neighbour correlations in these melt quench bioactive glasses, together with an analogous splitting of the Sr–O correlations; the correlations are attributed to the metal ions correlated either to bridging or to non-bridging oxygen atoms. Triple quantum (3Q) 43Ca MAS NMR corroborates the split Ca–O correlations. Successful simplification of the 2 < r (A) < 3 region via the difference method has also revealed two distinct Na environments. These environments are attributed to sodium correlated either to bridging or to nonbridging oxygen atoms. Complementary multinuclear MAS NMR, Sr K-edge EXAFS and X-ray diffraction data supports the structural model presented. The structural sites present will be intimately related to their release properties in physiological fluids such as plasma and saliva, and hence the bioactivity of the material. Detailed structural knowledge is therefore a prerequisite for optimising material design.

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In this paper, we propose a new edge-based matching kernel for graphs by using discrete-time quantum walks. To this end, we commence by transforming a graph into a directed line graph. The reasons of using the line graph structure are twofold. First, for a graph, its directed line graph is a dual representation and each vertex of the line graph represents a corresponding edge in the original graph. Second, we show that the discrete-time quantum walk can be seen as a walk on the line graph and the state space of the walk is the vertex set of the line graph, i.e., the state space of the walk is the edges of the original graph. As a result, the directed line graph provides an elegant way of developing new edge-based matching kernel based on discrete-time quantum walks. For a pair of graphs, we compute the h-layer depth-based representation for each vertex of their directed line graphs by computing entropic signatures (computed from discrete-time quantum walks on the line graphs) on the family of K-layer expansion subgraphs rooted at the vertex, i.e., we compute the depth-based representations for edges of the original graphs through their directed line graphs. Based on the new representations, we define an edge-based matching method for the pair of graphs by aligning the h-layer depth-based representations computed through the directed line graphs. The new edge-based matching kernel is thus computed by counting the number of matched vertices identified by the matching method on the directed line graphs. Experiments on standard graph datasets demonstrate the effectiveness of our new kernel.

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In this paper, we use the quantum Jensen-Shannon divergence as a means of measuring the information theoretic dissimilarity of graphs and thus develop a novel graph kernel. In quantum mechanics, the quantum Jensen-Shannon divergence can be used to measure the dissimilarity of quantum systems specified in terms of their density matrices. We commence by computing the density matrix associated with a continuous-time quantum walk over each graph being compared. In particular, we adopt the closed form solution of the density matrix introduced in Rossi et al. (2013) [27,28] to reduce the computational complexity and to avoid the cumbersome task of simulating the quantum walk evolution explicitly. Next, we compare the mixed states represented by the density matrices using the quantum Jensen-Shannon divergence. With the quantum states for a pair of graphs described by their density matrices to hand, the quantum graph kernel between the pair of graphs is defined using the quantum Jensen-Shannon divergence between the graph density matrices. We evaluate the performance of our kernel on several standard graph datasets from both bioinformatics and computer vision. The experimental results demonstrate the effectiveness of the proposed quantum graph kernel.