Orbital hyperfine interaction and qubit dephasing in carbon nanotube quantum dots

Hyperfine interaction (HF) is of key importance for the functionality of solid-state quantum information processing, as it affects qubit coherence and enables nuclear-spin quantum memories. In this work, we complete the theory of the basic hyperfine interaction mechanisms (Fermi contact, dipolar, orbital) in carbon nanotube quantum dots by providing a theoretical description of the orbital HF. We find that orbital HF induces an interaction between the nuclear spins of the nanotube lattice and the valley degree of freedom of the electrons confined in the quantum dot. We show that the resulting nuclear-spin--electron-valley interaction (i) is approximately of Ising type, (ii) is essentially local, in the sense that a radius- and dot-length-independent atomic interaction strength can be defined, and (iii) has an atomic interaction strength that is comparable to the combined strength of Fermi contact and dipolar interactions. We argue that orbital HF provides a new decoherence mechanism for single-electron valley qubits and spin-valley qubits in a range of multi-valley materials. We explicitly evaluate the corresponding inhomogeneous dephasing time $T_2^*$ for a nanotube-based valley qubit.Comment: 7 pages, 3 figure

Dislocation density and Burgers vector population in fiber-textured Ni thin films determined by high-resolution X-ray line profile analysis

Nanocrystalline Ni thin films have been produced by direct current electrodeposition with different additives and current density in order to obtain 〈100〉, 〈111〉 and 〈211〉 major fiber textures. The dislocation density, the Burgers vector population and the coherently scattering domain size distribution are determined by high-resolution X-ray diffraction line profile analysis. The substructure parameters are correlated with the strength of the films by using the combined Taylor and Hall–Petch relations. The convolutional multiple whole profile method is used to obtain the substructure parameters in the different coexisting texture components. A strong variation of the dislocation density is observed as a function of the deposition conditions.</jats:p

Testing goodness-of-fit of random graph models

Random graphs are matrices with independent 0, 1 elements with probabilities determined by a small number of parameters. One of the oldest model is the Rasch model where the odds are ratios of positive numbers scaling the rows and columns. Later Persi Diaconis with his coworkers rediscovered the model for symmetric matrices and called the model beta. Here we give goodnes-of-fit tests for the model and extend the model to a version of the block model introduced by Holland, Laskey, and Leinhard