Zeno effect of the open quantum system in the presence of 1/f noise

We study the quantum Zeno effect (QZE) and quantum anti-Zeno effect (QAZE) in a two-level system(TLS) interacting with an environment owning 1/f noise. Using a numerically exact method based on the thermo field dynamics(TFD) theory and the matrix product states(MPS), we obtain exact evolutions of the TLS and bath(environment) under repetitive measurements at both zero and finite temperatures. At zero temperature, we observe a novel transition from a pure QZE in the short time scale to a QZE-QAZE crossover in the long time scale, by considering the measurement induced non-Markvoian effect. At finite temperature, we exploit that the thermal fluctuation suppresses the decay of the survival probability in the short time scale, whereas it enhances the decay in the long time scale.Comment: 9 pages, 6 figure

On the quantification of the dissolved hydroxyl radicals in the plasma-liquid system using the molecular probe method

Hydroxyl (OH) radical is the most important reactive species produced by the plasma-liquid interactions, and the OH in the liquid phase (dissolved OH radical, OHdis) takes effect in many plasma-based applications due to its high reactivity. Therefore, the quantification of the OHdis in the plasma-liquid system is of great importance, and a molecular probe method usually used for the OHdis detection might be applied. Herein we investigate the validity of using the molecular probe method to estimate the [OHdis] in the plasma-liquid system. Dimethyl sulfoxide is used as the molecular probe to estimate the [OHdis] in an air plasma-liquid system, and the partial OHdis is related to the formed formaldehyde (HCHO) which is the OHdis-induced derivative. The analysis indicates that the true concentration of the OHdis should be estimated from the sum of three terms: the formed HCHO, the existing OH scavengers, and the OHdis generated H2O2. The results show that the measured [HCHO] needs to be corrected since the HCHO destruction is not negligible in the plasma-liquid system. We conclude from the results and the analysis that the molecular probe method generally underestimates the [OHdis] in the plasma-liquid system. If one wants to obtain the true concentration of the OHdis in the plasma-liquid system, one needs to know the destruction behavior of the OHdis-induced derivatives, the information of the OH scavengers (such as hydrated electron, atomic hydrogen besides the molecular probe), and also the knowledge of the OHdis generated H2O2.Comment: 17 pages, 4 figures,3 table

Complete phase diagram and topological properties of interacting bosons in one-dimensional superlattices

The interacting bosons in one-dimensional inversion-symmetric superlattices are investigated from the topological aspect. The complete phase diagram is obtained by an atomic-limit analysis and quantum Monte Carlo simulations and comprises three kinds of phases: superfluid, persisted charge-density-wave and Mott insulators, and emergent insulators in the presence of nearest-neighbor hoppings. We find that all emergent insulators are topological, which are characterized by the Berry phase $\pi$ and a pair of degenerate in-gap boundary states. The mechanism of the topological bosonic insulators is qualitatively discussed and the ones with higher fillings can be understood as a $\frac{1}{3}$-filling topological phase on a background of trivial charge-density-wave or Mott insulators.Comment: 6 pages, 8 figures. Accelpted for publication in Phys. Rev.

RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets

In this paper, we propose a class of robust stochastic subgradient methods for distributed learning from heterogeneous datasets at presence of an unknown number of Byzantine workers. The Byzantine workers, during the learning process, may send arbitrary incorrect messages to the master due to data corruptions, communication failures or malicious attacks, and consequently bias the learned model. The key to the proposed methods is a regularization term incorporated with the objective function so as to robustify the learning task and mitigate the negative effects of Byzantine attacks. The resultant subgradient-based algorithms are termed Byzantine-Robust Stochastic Aggregation methods, justifying our acronym RSA used henceforth. In contrast to most of the existing algorithms, RSA does not rely on the assumption that the data are independent and identically distributed (i.i.d.) on the workers, and hence fits for a wider class of applications. Theoretically, we show that: i) RSA converges to a near-optimal solution with the learning error dependent on the number of Byzantine workers; ii) the convergence rate of RSA under Byzantine attacks is the same as that of the stochastic gradient descent method, which is free of Byzantine attacks. Numerically, experiments on real dataset corroborate the competitive performance of RSA and a complexity reduction compared to the state-of-the-art alternatives.Comment: To appear in AAAI 201