Hamiltonian and measuring time for analog quantum search

We derive in this study a Hamiltonian to solve with certainty the analog quantum search problem analogue to the Grover algorithm. The general form of the initial state is considered. Since the evaluation of the measuring time for finding the marked state by probability of unity is crucially important in the problem, especially when the Bohr frequency is high, we then give the exact formula as a function of all given parameters for the measuring time.Comment: 5 page

The activation energy for GaAs/AlGaAs interdiffusion

Copyright 1997 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. This article appeared in Journal of Applied Physics 82, 4842 (1997) and may be found at

Scientific basis for safely shutting in the Macondo Well after the April 20, 2010 Deepwater Horizon blowout

As part of the government response to the Deepwater Horizon blowout, a Well Integrity Team evaluated the geologic hazards of shutting in the Macondo Well at the seafloor and determined the conditions under which it could safely be undertaken. Of particular concern was the possibility that, under the anticipated high shut-in pressures, oil could leak out of the well casing below the seafloor. Such a leak could lead to new geologic pathways for hydrocarbon release to the Gulf of Mexico. Evaluating this hazard required analyses of 2D and 3D seismic surveys, seafloor bathymetry, sediment properties, geophysical well logs, and drilling data to assess the geological, hydrological, and geomechanical conditions around the Macondo Well. After the well was successfully capped and shut in on July 15, 2010, a variety of monitoring activities were used to assess subsurface well integrity. These activities included acquisition of wellhead pressure data, marine multichannel seismic pro- files, seafloor and water-column sonar surveys, and wellhead visual/acoustic monitoring. These data showed that the Macondo Well was not leaking after shut in, and therefore, it could remain safely shut until reservoir pressures were suppressed (killed) with heavy drilling mud and the well was sealed with cement

Network Inference via the Time-Varying Graphical Lasso

Many important problems can be modeled as a system of interconnected entities, where each entity is recording time-dependent observations or measurements. In order to spot trends, detect anomalies, and interpret the temporal dynamics of such data, it is essential to understand the relationships between the different entities and how these relationships evolve over time. In this paper, we introduce the time-varying graphical lasso (TVGL), a method of inferring time-varying networks from raw time series data. We cast the problem in terms of estimating a sparse time-varying inverse covariance matrix, which reveals a dynamic network of interdependencies between the entities. Since dynamic network inference is a computationally expensive task, we derive a scalable message-passing algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve this problem in an efficient way. We also discuss several extensions, including a streaming algorithm to update the model and incorporate new observations in real time. Finally, we evaluate our TVGL algorithm on both real and synthetic datasets, obtaining interpretable results and outperforming state-of-the-art baselines in terms of both accuracy and scalability

A General SU(2) Formulation for Quantum Searching with Certainty

A general quantum search algorithm with arbitrary unitary transformations and an arbitrary initial state is considered in this work. To serach a marked state with certainty, we have derived, using an SU(2) representation: (1) the matching condition relating the phase rotations in the algorithm, (2) a concise formula for evaluating the required number of iterations for the search, and (3) the final state after the search, with a phase angle in its amplitude of unity modulus. Moreover, the optimal choices and modifications of the phase angles in the Grover kernel is also studied.Comment: 8 pages, 2 figure

A General Phase Matching Condition for Quantum Searching Algorithm

A general consideration on the phase rotations in quantum searching algorithm is taken in this work. As four phase rotations on the initial state, the marked states, and the states orthogonal to them are taken account, we deduce a phase matching condition for a successful search. The optimal options for these phase are obtained consequently.Comment: 3 pages, 3 figure

DIFFERENCES IN SEGMENTAL MOMENTUM TRANSFERS BETWEEN TWO STROKE POSTURES FOR TENNIS TWO-HANDED BACKHAND STROKE

Tennis stroke force depends on momentum transfer from racket to ball during ball-racket impact. Previous researchers study backhand stroke mechanics, focusing on comparison of one-handed and two-handed backhand stroke biomechanics (Reid & Elliott, 2002). This study investigated linear (LM) and angular momentum (AM) transfer from the trunk and upper extremities to the racket in open (OS) and square stances (SS) for different skill levels of players in the two-handed backhand stroke

A topological insulator surface under strong Coulomb, magnetic and disorder perturbations

Three dimensional topological insulators embody a newly discovered state of matter characterized by conducting spin-momentum locked surface states that span the bulk band gap as demonstrated via spin-resolved ARPES measurements . This highly unusual surface environment provides a rich ground for the discovery of novel physical phenomena. Here we present the first controlled study of the topological insulator surfaces under strong Coulomb, magnetic and disorder perturbations. We have used interaction of iron, with a large Coulomb state and significant magnetic moment as a probe to \textit{systematically test the robustness} of the topological surface states of the model topological insulator Bi$_2$Se$_3$. We observe that strong perturbation leads to the creation of odd multiples of Dirac fermions and that magnetic interactions break time reversal symmetry in the presence of band hybridization. We also present a theoretical model to account for the altered surface of Bi$_2$Se$_3$. Taken collectively, these results are a critical guide in manipulating topological surfaces for probing fundamental physics or developing device applications.Comment: 14 pages, 4 Figures. arXiv admin note: substantial text overlap with arXiv:1009.621

Toeplitz Inverse Covariance-Based Clustering of Multivariate Time Series Data

Subsequence clustering of multivariate time series is a useful tool for discovering repeated patterns in temporal data. Once these patterns have been discovered, seemingly complicated datasets can be interpreted as a temporal sequence of only a small number of states, or clusters. For example, raw sensor data from a fitness-tracking application can be expressed as a timeline of a select few actions (i.e., walking, sitting, running). However, discovering these patterns is challenging because it requires simultaneous segmentation and clustering of the time series. Furthermore, interpreting the resulting clusters is difficult, especially when the data is high-dimensional. Here we propose a new method of model-based clustering, which we call Toeplitz Inverse Covariance-based Clustering (TICC). Each cluster in the TICC method is defined by a correlation network, or Markov random field (MRF), characterizing the interdependencies between different observations in a typical subsequence of that cluster. Based on this graphical representation, TICC simultaneously segments and clusters the time series data. We solve the TICC problem through alternating minimization, using a variation of the expectation maximization (EM) algorithm. We derive closed-form solutions to efficiently solve the two resulting subproblems in a scalable way, through dynamic programming and the alternating direction method of multipliers (ADMM), respectively. We validate our approach by comparing TICC to several state-of-the-art baselines in a series of synthetic experiments, and we then demonstrate on an automobile sensor dataset how TICC can be used to learn interpretable clusters in real-world scenarios.Comment: This revised version fixes two small typos in the published versio