Adiabatic Quantum Computing with Phase Modulated Laser Pulses

Implementation of quantum logical gates for multilevel system is demonstrated through decoherence control under the quantum adiabatic method using simple phase modulated laser pulses. We make use of selective population inversion and Hamiltonian evolution with time to achieve such goals robustly instead of the standard unitary transformation language.Comment: 19 pages, 6 figures, submitted to JOP

Bayesian inference of physiologically meaningful parameters from body sway measurements

The control of the human body sway by the central nervous system, muscles, and conscious brain is of interest since body sway carries information about the physiological status of a person. Several models have been proposed to describe body sway in an upright standing position, however, due to the statistical intractability of the more realistic models, no formal parameter inference has previously been conducted and the expressive power of such models for real human subjects remains unknown. Using the latest advances in Bayesian statistical inference for intractable models, we fitted a nonlinear control model to posturographic measurements, and we showed that it can accurately predict the sway characteristics of both simulated and real subjects. Our method provides a full statistical characterization of the uncertainty related to all model parameters as quantified by posterior probability density functions, which is useful for comparisons across subjects and test settings. The ability to infer intractable control models from sensor data opens new possibilities for monitoring and predicting body status in health applications.Peer reviewe

Improved Error-Scaling for Adiabatic Quantum State Transfer

We present a technique that dramatically improves the accuracy of adiabatic state transfer for a broad class of realistic Hamiltonians. For some systems, the total error scaling can be quadratically reduced at a fixed maximum transfer rate. These improvements rely only on the judicious choice of the total evolution time. Our technique is error-robust, and hence applicable to existing experiments utilizing adiabatic passage. We give two examples as proofs-of-principle, showing quadratic error reductions for an adiabatic search algorithm and a tunable two-qubit quantum logic gate.Comment: 10 Pages, 4 figures. Comments are welcome. Version substantially revised to generalize results to cases where several derivatives of the Hamiltonian are zero on the boundar

EPR study of some rare-earth ions (Dy3+, Tb3+, and Nd3+) in YBa2Cu3O6-compound

We investigate the low temperature X-band electron paramagnetic resonance (EPR) of YBa2Cu3Ox compounds with x≅6.0 doped with Dy3+, Tb3+, and Nd3. The EPR spectra of Dy3+ and Tb3+ have been identified. The EPR of Tb3+ is used also to study the effect of suppression of high Tc superconductivity by doping with Tb3+. The EPR of Nd3+ is probably masked by the intense resonance of Cu2+. All experimental EPR results compare well with theoretical estimations. © 2003 Elsevier Science (USA). All rights reserved

Electron paramagnetic resonance of Tb 3+ ions in YBa 2Cu 3O 6

The first observation of electron paramagnetic resonance (EPR) of Tb 3+ doped into YBa 2 Cu 3O 6 is reported. EPR is used to determine the local symmetry of the rare-earth ion and to study the effect of suppression of high-T c superconductivity by doping. The distance between the lowest singlets of Tb 3+ ion Δ ≅ 7.1 GHz ≡ 0.24 cm -1 and g-factor g ∥ ≃ 17.9 have been estimated from measurements. Both these parameters are in a good agreement with the corresponding calculated values. No evidence of Tb 4+ ions was found. © 2000 Plenum Publishing Corporation

Parameter estimation for biochemical reaction networks using Wasserstein distances

We present a method for estimating parameters in stochastic models of biochemical reaction networks by fitting steady-state distributions using Wasserstein distances. We simulate a reaction network at different parameter settings and train a Gaussian process to learn the Wasserstein distance between observations and the simulator output for all parameters. We then use Bayesian optimization to find parameters minimizing this distance based on the trained Gaussian process. The effectiveness of our method is demonstrated on the three-stage model of gene expression and a genetic feedback loop for which moment-based methods are known to perform poorly. Our method is applicable to any simulator model of stochastic reaction networks, including Brownian Dynamics.Comment: 22 pages, 8 figures. Slight modifications/additions to the text; added new section (Section 4.4) and Appendi

Generation and Suppression of Decoherence in Artificial Environment for Qubit System

It is known that a quantum system with finite degrees of freedom can simulate a composite of a system and an environment if the state of the hypothetical environment is randomized by external manipulation. We show theoretically that any phase decoherence phenomena of a single qubit can be simulated with a two-qubit system and demonstrate experimentally two examples: one is phase decoherence of a single qubit in a transmission line, and the other is that in a quantum memory. We perform NMR experiments employing a two-spin molecule and clearly measure decoherence for both cases. We also prove experimentally that the bang-bang control efficiently suppresses decoherence.Comment: 25 pages, 7 figures; added reference

Almost uniform sampling via quantum walks

Many classical randomized algorithms (e.g., approximation algorithms for #P-complete problems) utilize the following random walk algorithm for {\em almost uniform sampling} from a state space $S$ of cardinality $N$: run a symmetric ergodic Markov chain $P$ on $S$ for long enough to obtain a random state from within $\epsilon$ total variation distance of the uniform distribution over $S$. The running time of this algorithm, the so-called {\em mixing time} of $P$, is $O(\delta^{-1} (\log N + \log \epsilon^{-1}))$, where $\delta$ is the spectral gap of $P$. We present a natural quantum version of this algorithm based on repeated measurements of the {\em quantum walk} $U_t = e^{-iPt}$. We show that it samples almost uniformly from $S$ with logarithmic dependence on $\epsilon^{-1}$ just as the classical walk $P$ does; previously, no such quantum walk algorithm was known. We then outline a framework for analyzing its running time and formulate two plausible conjectures which together would imply that it runs in time $O(\delta^{-1/2} \log N \log \epsilon^{-1})$ when $P$ is the standard transition matrix of a constant-degree graph. We prove each conjecture for a subclass of Cayley graphs.Comment: 13 pages; v2 added NSF grant info; v3 incorporated feedbac

Quantum random walks with history dependence

We introduce a multi-coin discrete quantum random walk where the amplitude for a coin flip depends upon previous tosses. Although the corresponding classical random walk is unbiased, a bias can be introduced into the quantum walk by varying the history dependence. By mixing the biased random walk with an unbiased one, the direction of the bias can be reversed leading to a new quantum version of Parrondo's paradox.Comment: 8 pages, 6 figures, RevTe