Synthesis and Bulk Properties of Oxychloride Superconductor Ca2-xNaxCuO2Cl2

Polycrystalline samples and submillimeter size single crystals of Na-doped Ca2CuO2Cl2 have been synthesized under high pressure. A series of experiments showed that the Na content depends not only on the pressure during the synthesis but also on the synthesis temperature and time. From a comparison of the Na-CCOC data with those of structurally related La214 cuprate superconductors we concluded that chlorine at the apical site is less effective that oxygen in supplying charge carriers to the CuO2 plans. As a result, the coupling between the CuO2 planes is weakened, the transition temperature Tc is reduced and the anisotropic nature is enhanced.Comment: 7 pages, 7 figures, 1 table, presenthed at the Eucas 2007 conference. Accepted for "Journal of Physics: Conference Series (JPCS)" 2008 and European News Forum, Issue 3 (2008

Load-Balancing for Parallel Delaunay Triangulations

Computing the Delaunay triangulation (DT) of a given point set in $\mathbb{R}^D$ is one of the fundamental operations in computational geometry. Recently, Funke and Sanders (2017) presented a divide-and-conquer DT algorithm that merges two partial triangulations by re-triangulating a small subset of their vertices - the border vertices - and combining the three triangulations efficiently via parallel hash table lookups. The input point division should therefore yield roughly equal-sized partitions for good load-balancing and also result in a small number of border vertices for fast merging. In this paper, we present a novel divide-step based on partitioning the triangulation of a small sample of the input points. In experiments on synthetic and real-world data sets, we achieve nearly perfectly balanced partitions and small border triangulations. This almost cuts running time in half compared to non-data-sensitive division schemes on inputs exhibiting an exploitable underlying structure.Comment: Short version submitted to EuroPar 201

On compatibility and improvement of different quantum state assignments

When Alice and Bob have different quantum knowledges or state assignments (density operators) for one and the same specific individual system, then the problems of compatibility and pooling arise. The so-called first Brun-Finkelstein-Mermin (BFM) condition for compatibility is reobtained in terms of possessed or sharp (i. e., probability one) properties. The second BFM condition is shown to be generally invalid in an infinite-dimensional state space. An argument leading to a procedure of improvement of one state assifnment on account of the other and vice versa is presented.Comment: 8 page

Hamiltonian Determination with Restricted Access in Transverse Field Ising Chain

We propose a method to evaluate parameters in the Hamiltonian of the Ising chain under site-dependent transverse fields, with a proviso that we can control and measure one of the edge spins only. We evaluate the eigenvalues of the Hamiltonian and the time-evoultion operator exactly for a 3-spin chain, from which we obtain the expectation values of $\sigma_x$ of the first spin. The parameters are found from the peak positions of the Fourier transform of the expectation value. There are four assumptions in our method, which are mild enough to be satisfied in many physical systems.Comment: 15pages, 4 figure

Anisotropy of Superconducting Single Crystal SmFeAsO0.8F0.2 Studied by Torque Magnetometry

Single crystals of the oxypnictide superconductor SmFeAsO0.8F0.2 with T c≃45(1) K were investigated by torque magnetometry. The crystals of mass ≤0.1μg were grown by a high-pressure cubic anvil technique. The use of a high-sensitive piezoresistive torque sensor made it possible to study the anisotropic magnetic properties of these tiny crystals. The anisotropy parameter γ was found to be field independent, but varies strongly with temperature ranging from γ≃8 at T≲T c to γ≃23 at T≃0.4T c. This unusual behavior of γ signals unconventional superconductivit

Robust Online Hamiltonian Learning

In this work we combine two distinct machine learning methodologies, sequential Monte Carlo and Bayesian experimental design, and apply them to the problem of inferring the dynamical parameters of a quantum system. We design the algorithm with practicality in mind by including parameters that control trade-offs between the requirements on computational and experimental resources. The algorithm can be implemented online (during experimental data collection), avoiding the need for storage and post-processing. Most importantly, our algorithm is capable of learning Hamiltonian parameters even when the parameters change from experiment-to-experiment, and also when additional noise processes are present and unknown. The algorithm also numerically estimates the Cramer-Rao lower bound, certifying its own performance.Comment: 24 pages, 12 figures; to appear in New Journal of Physic

Bell's Theorem from Moore's Theorem

It is shown that the restrictions of what can be inferred from classically-recorded observational outcomes that are imposed by the no-cloning theorem, the Kochen-Specker theorem and Bell's theorem also follow from restrictions on inferences from observations formulated within classical automata theory. Similarities between the assumptions underlying classical automata theory and those underlying universally-unitary quantum theory are discussed.Comment: 12 pages; to appear in Int. J. General System