Generating entangled states from coherent states in circuit-QED

Entangled states are self-evidently important to a wide range of applications in quantum communication and quantum information processing. We propose an efficient and convenient two-step protocol for generating Bell states and NOON states of two microwave resonators from merely coherent states. In particular, we derive an effective Hamiltonian for resonators coupled to a superconducting $\Lambda$-type qutrit in the dispersive regime. By the excitation-number-dependent Stark shifts of the qutrit transition frequencies, we are able to individually control the amplitudes of specified Fock states of the resonators associated with relevant qutrit transition, using carefully tailored microwave drive signals. Thereby an arbitrary bipartite entangled state in Fock space can be generated by a typical evolution-and-measurement procedure. We analysis the undesired state transitions and the robustness of our protocol against the systematic errors from the microwave driving intensity and frequency, the quantum decoherence of all components, and the crosstalk of two resonators. In addition, we demonstrate that our protocol can be extended to a similar scenario with a $\Xi$-type qutrit.Comment: 13 pages, 11 figures, 1 tabl

A method based on multiscale base-scale entropy and random forests for roller bearings faults diagnosis

A method based on multiscale base-scale entropy (MBSE) and random forests (RF) for roller bearings faults diagnosis is presented in this study. Firstly, the roller bearings vibration signals were decomposed into base-scale entropy (BSE), sample entropy (SE) and permutation entropy (PE) values by using MBSE, multiscale sample entropy (MSE) and multiscale permutation entropy (MPE) under different scales. Then the computation time of the MBSE/MSE/MPE methods were compared. Secondly, the entropy values of BSE, SE, and PE under different scales were regarded as the input of RF and SVM optimized by particle swarm ion (PSO) and genetic algorithm (GA) algorithms for fulfilling the fault identification, and the classification accuracy was utilized to verify the effect of the MBSE/MSE/MPE methods by using RF/PSO/GA-SVM models. Finally, the experiment result shows that the computational efficiency and classification accuracy of MBSE method are superior to MSE and MPE with RF and SVM

2-Chloro­methyl-1-methyl-1,3-benzimidazole

The title compound, C9H9ClN2, was prepared from the reaction of N-methyl­benzene-1,2-diamine and 2-chloro­acetic acid in boiling 6 M hydro­chloric acid. The benzimidazole unit is approximately planar, the largest deviation from the mean plane being 0.008 (1) Å. The Cl atom is displaced by 1.667 (2) Å from this plane. The methyl group is statistically disordered with equal occupancy

Huber Kalman Filter for Wi-Fi based Vehicle Driver\u27s Respiration Detection

The use of breath detection in vehicles can reduce the number of vehicular accidents caused by drivers in poor physical condition. Prior studies of contactless respiration detection mainly targeted a static person. However, there are emerging applications to sense a driver, with emphasis on contactless methods. For example, being able to detect a driver\u27s respiration while driving by using a vehicular Wi-Fi system can significantly enhance driving safety. The sensing system can be mounted on the back of the driver\u27s seat, and it can sense the tiny chest displacement of the driver via Wi-Fi signals. The body displacement and car vibrations could introduce significant noise in the sensed signal. The noise then needs to be filtered to obtain the driver\u27s respiration. In this work, the noise in the sensed signal is proposed to be reduced using a Huber Kalman filter to restore the original respiration curve. Through several experiments in terms of different drivers, different car models, multiple passengers, and abnormal breathing, we demonstrate the accuracy and robustness of the Huber Kalman filter in driver\u27s respiration