Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses. © 2012 American Physical Society.

Singlet-state creation and universal quantum computation in NMR using a genetic algorithm

The experimental implementation of a quantum algorithm requires the decomposition of unitary operators. Here we treat unitary-operator decomposition as an optimization problem, and use a genetic algorithm-a global-optimization method inspired by nature's evolutionary process-for operator decomposition. We apply this method to NMR quantum information processing, and find a probabilistic way of performing universal quantum computation using global hard pulses. We also demonstrate the efficient creation of the singlet state (a special type of Bell state) directly from thermal equilibrium, using an optimum sequence of pulses.

High resolution methyl selective C-13-NMR of proteins in solution and solid state

New C-13-detected NMR experiments have been devised for molecules in solution and solid state, which provide chemical shift correlations of methyl groups with high resolution, selectivity and sensitivity. The experiments achieve selective methyl detection by exploiting the one bond J-coupling between the C-13-methyl nucleus and its directly attached C-13 spin in a molecule. In proteins such correlations edit the C-13-resonances of different methyl containing residues into distinct spectral regions yielding a high resolution spectrum. This has a range of applications as exemplified for different systems such as large proteins, intrinsically disordered polypeptides and proteins with a paramagnetic centre.

State Dependent Attempt Rate modeling of single cell IEEE 802.11 WLANs with homogeneous nodes and Poisson packet arrivals

We study a State Dependent Attempt Rate (SDAR) approximation to model M queues (one queue per node) served by the Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA) protocol as standardized in the IEEE 802.11 Distributed Coordination Function (DCF). The approximation is that, when n of the M queues are non-empty, the (transmission) attempt probability of each of the n non-empty nodes is given by the long-term (transmission) attempt probability of n saturated nodes. With the arrival of packets into the M queues according to independent Poisson processes, the SDAR approximation reduces a single cell with non-saturated nodes to a Markovian coupled queueing system. We provide a sufficient condition under which the joint queue length Markov chain is positive recurrent. For the symmetric case of equal arrival rates and finite and equal buffers, we develop an iterative method which leads to accurate predictions for important performance measures such as collision probability, throughput and mean packet delay. We replace the MAC layer with the SDAR model of contention by modifying the NS-2 source code pertaining to the MAC layer, keeping all other layers unchanged. By this model-based simulation technique at the MAC layer, we achieve speed-ups (w.r.t. MAC layer operations) up to 5.4. Through extensive model-based simulations and numerical results, we show that the SDAR model is an accurate model for the DCF MAC protocol in single cells. (C) 2012 Elsevier B.V. All rights reserved.

Nonlinear dynamic state estimation in instrumented structures with conditionally linear Gaussian substructures

Many problems of state estimation in structural dynamics permit a partitioning of system states into nonlinear and conditionally linear substructures. This enables a part of the problem to be solved exactly, using the Kalman filter, and the remainder using Monte Carlo simulations. The present study develops an algorithm that combines sequential importance sampling based particle filtering with Kalman filtering to a fairly general form of process equations and demonstrates the application of a substructuring scheme to problems of hidden state estimation in structures with local nonlinearities, response sensitivity model updating in nonlinear systems, and characterization of residual displacements in instrumented inelastic structures. The paper also theoretically demonstrates that the sampling variance associated with the substructuring scheme used does not exceed the sampling variance corresponding to the Monte Carlo filtering without substructuring. (C) 2012 Elsevier Ltd. All rights reserved.

Catalysis of tRNA Aminoacylation: Single Turnover to Steady-State Kinetics of tRNA Synthetases

Aminoacyl-tRNA synthetases (aaRS) catalyze the bimolecular association reaction between amino acid and tRNA by specifically and unerringly choosing the cognate amino acid and tRNA. There are two classes of such synthetases that perform tRNA-aminoacylation reaction. Interestingly, these two classes of aminoacyl-tRNA synthetases differ not only in their structures but they also exhibit remarkably distinct kinetics under pre-steady-state condition. The class I synthetases show initial burst of product formation followed by a slower steady-state rate. This has been argued to represent the influence of slow product release. In contrast, there is no burst in the case of class H enzymes. The tight binding of product with enzyme for class I enzymes is correlated with the enhancement of rate in presence of elongation factor. EF-TU. In spite of extensive experimental studies, there is no detailed theoretical analysis that can provide a quantitative understanding of this important problem. In this article, we present a theoretical investigation of enzyme kinetics for both classes of aminoacyl-tRNA synthetases. We present an augmented kinetic scheme and then employ the methods of time-dependent probability statistics to obtain expressions for the first passage time distribution that gives both the time-dependent and the steady-state rates. The present study quantitatively explains all the above experimental observations. We propose an alternative path way in the case of class II enzymes showing the tRNA-dependent amino acid activation and the discrepancy between the single-turnover and steady-state rate.

Decentralized linear quadratic power system stabilizers for multi-machine power systems

Linear quadratic stabilizers are well-known for their superior control capabilities when compared to the conventional lead-lag power system stabilizers. However, they have not seen much of practical importance as the state variables are generally not measurable; especially the generator rotor angle measurement is not available in most of the power plants. Full state feedback controllers require feedback of other machine states in a multi-machine power system and necessitate block diagonal structure constraints for decentralized implementation. This paper investigates the design of Linear Quadratic Power System Stabilizers using a recently proposed modified Heffron-Phillip's model. This model is derived by taking the secondary bus voltage of the step-up transformer as reference instead of the infinite bus. The state variables of this model can be obtained by local measurements. This model allows a coordinated linear quadratic control design in multi machine systems. The performance of the proposed controller has been evaluated on two widely used multi-machine power systems, 4 generator 10 bus and 10 generator 39 bus systems. It has been observed that the performance of the proposed controller is superior to that of the conventional Power System Stabilizers (PSS) over a wide range of operating and system conditions.