Frequentist and Bayesian Quantum Phase Estimation

Frequentist and Bayesian phase estimation strategies lead to conceptually different results on the state of knowledge about the true value of the phase shift. We compare the two frameworks and their sensitivity bounds to the estimation of an interferometric phase shift limited by quantum noise, considering both the cases of a fixed and a fluctuating parameter. We point out that frequentist precision bounds, such as the Cram\`er-Rao bound, for instance, do not apply to Bayesian strategies and vice-versa. Similarly, bounds for fluctuating parameters make no statement about the estimation of a fixed parameter.Comment: 4 figure

The Relationship Between Observers' Self-Attractiveness and Preference for Physical Dimorphism: A Meta-Analysis

Background: Many studies have reported an association between observers' self-attractiveness and their preference for sexual dimorphism across different physical domains, including the face, voice, and body. However, the results of these studies are inconsistent. Here, a meta-analysis was conducted to estimate the association between observers' own attractiveness and their dimorphic preference.Methods: Major electronic databases including PsycINFO, Web of Science, PubMed, ProQuest, and Google Scholar were searched during April 2017 (the first time) and April 2018 (the second time). The effect size computation and moderating effect analyses were conducted separately for masculine and feminine preferences.Results: We identified 5,359 references, of which we included 25 studies (x = 55, x = number of the effect size) with 6,853 participants in the meta-analysis. Across these studies, the correlation between observers' own attractiveness and their sexual dimorphic preference was 0.095 (x = 55) and that for preference for masculinity (x = 39) and femininity (x = 16) were 0.102 and 0.076, respectively. The results of the funnel plot, Egger's regression method, and fail-safe number suggested that there was no obvious publication bias. The relationship depended on the relationship context (short or long-term), opposite or same sex (the gender of the observer and host), measures of observers' self-attractiveness (subject or objective), and preference task (e.g., attractiveness rating, forced-choice, and face sequence test). Furthermore, for female participants, using a hormonal contraceptive also influenced their masculinity preference. The effect size for the preference for a masculine body and voice was larger than that for facial masculinity.Conclusion: We found a small but significant correlation between self-attractiveness and physical dimorphic preference, the relationship was moderated by the relationship context, same/opposite-sex, and contraceptive using. These three moderating effects represented the observer's trade-off on good genes, good provider and good father (3Gs) consistent with the life history strategies. Besides, measurement of observers' attractiveness, type of preference task and stimuli may also involve the relationship

A new adaptive backpropagation algorithm based on Lyapunov stability theory for neural networks

A new adaptive backpropagation (BP) algorithm based on Lyapunov stability theory for neural networks is developed in this paper. It is shown that the candidate of a Lyapunov function V(k) of the tracking error between the output of a neural network and the desired reference signal is chosen first, and the weights of the neural network are then updated, from the output layer to the input layer, in the sense that DeltaV(k)=V(k)-V(k-1)<0. The output tracking error can then asymptotically converge to zero according to Lyapunov stability theory. Unlike gradient-based BP training algorithms, the new Lyapunov adaptive BP algorithm in this paper is not used for searching the global minimum point along the cost-function surface in the weight space, but it is aimed at constructing an energy surface with a single global minimum point through the adaptive adjustment of the weights as the time goes to infinity. Although a neural network may have bounded input disturbances, the effects of the disturbances can be eliminated, and asymptotic error convergence can be obtained. The new Lyapunov adaptive BP algorithm is then applied to the design of an adaptive filter in the simulation example to show the fast error convergence and strong robustness with respect to large bounded input disturbance

An edge-based matching kernel through discrete-time quantum walks

In this paper, we propose a new edge-based matching kernel for graphs by using discrete-time quantum walks. To this end, we commence by transforming a graph into a directed line graph. The reasons of using the line graph structure are twofold. First, for a graph, its directed line graph is a dual representation and each vertex of the line graph represents a corresponding edge in the original graph. Second, we show that the discrete-time quantum walk can be seen as a walk on the line graph and the state space of the walk is the vertex set of the line graph, i.e., the state space of the walk is the edges of the original graph. As a result, the directed line graph provides an elegant way of developing new edge-based matching kernel based on discrete-time quantum walks. For a pair of graphs, we compute the h-layer depth-based representation for each vertex of their directed line graphs by computing entropic signatures (computed from discrete-time quantum walks on the line graphs) on the family of K-layer expansion subgraphs rooted at the vertex, i.e., we compute the depth-based representations for edges of the original graphs through their directed line graphs. Based on the new representations, we define an edge-based matching method for the pair of graphs by aligning the h-layer depth-based representations computed through the directed line graphs. The new edge-based matching kernel is thus computed by counting the number of matched vertices identified by the matching method on the directed line graphs. Experiments on standard graph datasets demonstrate the effectiveness of our new kernel

A quantum Jensen-Shannon graph kernel using discrete-time quantum walks

In this paper, we develop a new graph kernel by using the quantum Jensen-Shannon divergence and the discrete-time quantum walk. To this end, we commence by performing a discrete-time quantum walk to compute a density matrix over each graph being compared. For a pair of graphs, we compare the mixed quantum states represented by their density matrices using the quantum Jensen-Shannon divergence. With the density matrices for a pair of graphs to hand, the quantum graph kernel between the pair of graphs is defined by exponentiating the negative quantum Jensen-Shannon divergence between the graph density matrices. We evaluate the performance of our kernel on several standard graph datasets, and demonstrate the effectiveness of the new kernel

Numerical model for geothermal energy utilization from double pipe heat exchanger in abandoned oil wells

  The number of abandonded wells are increasing in the late period of oilfield development. The utilization of these abandonded oil wells is promising and environment-friendly for geothermal development. In this study, a numerical model for geothermal heating is derived from a double pipe heat exchanger in abandoned oil wells. The main influencing factors of injection rate, injection time, and the types of filler in casing annulus on temperature profiles and outlet temperature have been considered in this model. The influences of injection rate on heat-mining rate are then discussed. Results show that the double pipe heat exchanger can gain higher temperature at the outlet when the casing annulus is filled by liquid other than dry cement under the given parameter combination. The outlet temperature decreases with the increase in injection rate and injection time. The temperature rapidly decreases in the first 40 days during the injection process. The balance between heat mining rate and outlet temperature is important for evaluating a double pipe heat exchanger in abandoned oil wells. This work may provide a useful tool for a field engineer to estimate the temperature of liquid in wellhead and evaluate the heat transfer efficiency for double pipe heat exchanger in abandoned oil wells.Cited as: Lin, Z., Liu, K., Liu, J., Geng, D., Ren, K., Zheng, Z. Numerical model for geothermal energy utilization from double pipe heat exchanger in abandoned oil wells. Advances in Geo-Energy Research, 2021, 5(2): 212-221, doi: 10.46690/ager.2021.02.1

High optical transmittance of aluminum ultrathin film with hexagonal nanohole arrays as transparent electrode

We fabricate samples of aluminum ultrathin films with hexagonal nanohole arrays and characterize the transmission performance. High optical transmittance larger than 60% over a broad wavelength range from 430 nm to 750 nm is attained experimentally. The Fano-type resonance of the excited surface plasmon plaritons and the directly transmitted light attribute to both of the broadband transmission enhancement and the transmission suppression dips

Quantum kernels for unattributed graphs using discrete-time quantum walks

In this paper, we develop a new family of graph kernels where the graph structure is probed by means of a discrete-time quantum walk. Given a pair of graphs, we let a quantum walk evolve on each graph and compute a density matrix with each walk. With the density matrices for the pair of graphs to hand, the kernel between the graphs is defined as the negative exponential of the quantum Jensen–Shannon divergence between their density matrices. In order to cope with large graph structures, we propose to construct a sparser version of the original graphs using the simplification method introduced in Qiu and Hancock (2007). To this end, we compute the minimum spanning tree over the commute time matrix of a graph. This spanning tree representation minimizes the number of edges of the original graph while preserving most of its structural information. The kernel between two graphs is then computed on their respective minimum spanning trees. We evaluate the performance of the proposed kernels on several standard graph datasets and we demonstrate their effectiveness and efficiency

A Fast Diagnosis Method for Both IGBT Faults and Current Sensor Faults in Grid-Tied Three-Phase Inverters With Two Current Sensors

© 1986-2012 IEEE. This article considers fault detection in the case of a three-phase three-wire (3P3W) inverter, when only two current sensors are used to save cost or due to a faulty current sensor. With two current sensors, there is no current method addressing the diagnosis of both IGBT open-circuit (OC) faults and current sensor faults. In order to solve this problem, this article proposes a method which innovatively combines two kinds of diagnosis variables, line voltage deviations and phase voltage deviations. The unique faulty characteristics of diagnosis variables for each fault are extracted and utilized to distinguish the fault. Using an average model, the method only needs the signals already available in the controller. Both IGBT OC faults and current sensor faults can be detected quickly in inverter mode and rectifier mode, so that the converter can be protected in a timely way to avoid further damages. In addition, error-adaptive thresholds are adopted to make the method robust. Effects such as system unbalance are analyzed to ensure that the method is robust and feasible. Simulation and experimental results are used to verify and validate the effectiveness of the method

Catalytic, Kinetic, and Mechanistic Insights into the Fixation of CO2 with Epoxides Catalyzed by Phenol-Functionalized Phosphonium Salts

A series of hydroxy-functionalized phosphonium salts were studied as bifunctional catalysts for the conversion of CO2 with epoxides under mild and solvent-free conditions. The reaction in the presence of a phenol-based phosphonium iodide proceeded via a first order rection kinetic with respect to the substrate. Notably, in contrast to the aliphatic analogue, the phenol-based catalyst showed no product inhibition. The temperature dependence of the reaction rate was investigated, and the activation energy for the model reaction was determined from an Arrhenius-plot (Ea =39.6 kJ mol-1 ). The substrate scope was also evaluated. Under the optimized reaction conditions, 20 terminal epoxides were converted at room temperature to the corresponding cyclic carbonates, which were isolated in yields up to 99 %. The reaction is easily scalable and was performed on a scale up to 50 g substrate. Moreover, this method was applied in the synthesis of the antitussive agent dropropizine starting from epichlorohydrin and phenylpiperazine. Furthermore, DFT calculations were performed to rationalize the mechanism and the high efficiency of the phenol-based phosphonium iodide catalyst. The calculation confirmed the activation of the epoxide via hydrogen bonding for the iodide salt, which facilitates the ring-opening step. Notably, the effective Gibbs energy barrier regarding this step is 97 kJ mol-1 for the bromide and 72 kJ mol-1 for the iodide salt, which explains the difference in activity