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It is well known that quantum algorithms may solve problems efficiently that are intractable using conventional algorithms. Quantum algorithms can be designed with a set of universal quantum gates that transform input states into desired output states. However, designing quantum algorithms that transform states in desired ways is challenging due to its complexity. In this paper, we propose a machine learning framework for the transformation of unknown states into their corresponding target states. Specifically, a parameterized quantum circuit learns a given task by tuning its parameters. After the learning is done, the circuit is competent for the quantum task. This allows us to circumvent cumbersome circuit design based on universal quantum gates. If perfect transformation is forbidden by quantum theory, an optimal transformation can be obtained in terms of fidelity. This provides a research method to study various quantum no-go theorems that characterize the intrinsic gap between quantum and classical information. As examples, quantum state rotation and quantum state cloning are studied using numerical simulations. We also show the good robustness of our machine learning framework to corrupted training data, which is a very nice property for physical implementation on near-term noisy intermediate-scale quantum devices. [ABSTRACT FROM AUTHOR]

Machining distortion has been a long-term obstacle in the machining of aircraft monolithic parts. Furthermore, its stability has to be considered. The machining distortion stability represents the fluctuation degree of the machining distortion. This paper investigates the evolution of elastic energy induced by initial residual stress inside materials, revealing that this evolution directly affects machining distortion. In this paper, the concept of machining distortion stability and bending potential energy is defined. By analyzing bending potential energy releasing, this study proposes a novel method for improving machining distortion stability through optimization of material removal sequence. Numerical simulation and milling experiments are performed to verify and validate the model, respectively. The results indicate that the machining distortion stability is significantly improved when optimized material removal sequence is applied. By controlling the machining distortion stability, the final distortion can be further reduced via re-machining the machining datum at the beginning of the finishing stage. [ABSTRACT FROM AUTHOR]

Modularity of machine tool, an engineering technology that deals with cost-effective and quick reaction to all the market changes, is urgently needed in the modern manufacturing companies. However, a general accuracy analytical method of modular machine tool is seldom concerned. The basic module error of the machine tool is synthesized by the theory of computer aided multi-body system, a universal accuracy model for modular machine tools has been established. The developed mathematical model is used to calculate and predict the resultant position error at the tool–workpiece interface for error compensation. Results show the method based on the computer aided multi-body system only depends on the motion of module and fits for rapid modeling of computer technology, and error factors such as thermal expansion and deforming under loads can be involved in the analysis processing except the geometric error. [ABSTRACT FROM AUTHOR]

We study the quantum holographic imaging of one-dimensional electromagnetically induced grating created by a strong standing wave in an atomic medium. Entangled photon pairs, generated in a spontaneous parametric down-conversion process, are employed as the imaging light to realize coincidence recording. By theoretical analysis and numerical simulation, we find that both the amplitude and phase information of the object can be imaged with the characteristic of imaging nonlocally and of arbitrarily controllable image variation in size. [ABSTRACT FROM AUTHOR]

The regional verification of soil moisture is a vital step in evaluating and improving numerical model performance and utilizing forecast results. Currently, even with improved spatial and temporal resolutions of numerical model, verification methods for soil moisture data still rely on the traditional intensity verification parameters, such as mean error (ME) and root-mean-squared error (RMSE). Those methods provide only incomplete and sometimes inaccurate messages and thus hinder a proper evaluation of a forecast model. The SAL method is an object-based regional verification method with respect to precipitation forecasts. Based on the SAL method, a novel object-based method (SAL-DN) is proposed here, which can be used to test regional soil moisture. Both the ideal experiment and real experiment show that the SAL-DN method can reveal the differences between the observed and forecast soil moisture in three aspects: structure, amplitude, and location, and the results can reflect the actual situation. Furthermore, compared with the SAL method, the SAL-DN method is also capable of verifying physical quantities with high-value and low-value centers like temperature. Therefore, the SAL-DN method enhances verification accuracy and can be applied widely. [ABSTRACT FROM AUTHOR]

We propose a deterministic scheme for preparing any number of distant atoms in a W state. The atoms are respectively trapped in spatially separated optical cavities connected by the fibers. Our scheme utilizes the adiabatic passage along dark state, and is robust against the atomic spontaneous emission and the decays of cavities. The numerical simulation of entanglement fidelity shows that the losses of fibers can be negligible under certain conditions. All of the advantages make the protocol more practical than previous ones. [ABSTRACT FROM AUTHOR]