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Potassium fluoroboratoberyllate KBe2BO3F2 (KBBF) has been revealed theoretically and experimentally as a novel ultraviolet nonlinear optical crystal, but it is found to be very difficult to grow in a large size, because of the weak binding interaction between the (Be2BO3)∞ units, which leads to an apparent layer habit in the growth. By using a molecular engineering approach, oxygen bridges when brought in to strengthen the binding between the infinite units are found to be useful to overcome the above shortcoming of KBBF, and in the light of it another new ultraviolet nonlinear optical crystal—strontium boratoberyllate Sr2Be2B2O7 (SBBO) has been discovered. The linear optical properties of SBBO are similar to KBBF’s, but its nonlinear optical properties are better than that of the latter. d22(SBBO)≂d22(β-BaB2O4), which is two times higher than d11 of KBBF. SBBO has very good mechanical properties, and it is also not deliquescent. So SBBO is expected to have great potential for the application in ultraviolet nonlinear optical devices.

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This article presents an up-to-date tutorial review of nonlinear Bayesian estimation. State estimation for nonlinear systems has been a challenge encountered in a wide range of engineering fields, attracting decades of research effort. To date, one of the most promising and popular approaches is to view and address the problem from a Bayesian probabilistic perspective, which enables estimation of the unknown state variables by tracking their probabilistic distribution or statistics (e.g., mean and covariance) conditioned on the system's measurement data. This article offers a systematic introduction of the Bayesian state estimation framework and reviews various Kalman filtering (KF) techniques, progressively from the standard KF for linear systems to extended KF, unscented KF and ensemble KF for nonlinear systems. It also overviews other prominent or emerging Bayesian estimation methods including the Gaussian filtering, Gaussian-sum filtering, particle filtering and moving horizon estimation and extends the discussion of state estimation forward to more complicated problems such as simultaneous state and parameter/input estimation.

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Grover's algorithm is one of the most famous algorithms which explicitly demonstrates how the quantum nature can be utilized to accelerate the searching process. In this work, Grover's quantum search problem is mapped to a time-optimal control problem. Resorting to Pontryagin's Minimum Principle we find that the time-optimal solution has the bang-singular-bang structure. This structure can be derived naturally, without integrating the differential equations, using the geometric control technique where Hamiltonians in the Schrödinger's equation are represented as vector fields. In view of optimal control, Grover's algorithm uses the bang-bang protocol to approximate the optimal protocol with a minimized number of bang-to-bang switchings to reduce the query complexity. Our work provides a concrete example how Pontryagin's Minimum Principle is connected to quantum computation, and offers insight into how a quantum algorithm can be designed.PACS numbers: I. INTRODUCTIONQuantum computation deliberately uses the quantum-mechanical phenomena, such as superposition and entanglement, to reduce the computation time or the number of queries to accomplish certain tasks [1,2]. Well known quantum algorithms include Shor's algorithm for factoring [3] and Grover's algorithm for searching an unstructured database or an unordered list [4,5]. While the standard paradigm for quantum computation involves a discrete sequence of unitary logic gates [3-9] there exists another paradigm, pioneered by Farhi and Gutmann [10], where the quantum register evolves under some designed Hamiltonian which can vary continuously in time [10-13]. The concept of "continuous-time" quantum computation explicitly allows established physics principles such as the adiabatic theorem [14-16] and the Trotter-Suzuki decomposition [17,18] to guide how quantum algorithms can be designed. The adiabatic theorem is, for example, the foundation of the quantum annealing technique [11,[19][20][21][22] and the fast, non-adiabatic evolution is found to be helpful for other problems [23,24]. More recently, there are quantum algorithms based on the variational principle, notably the Variational Quantum Eigensolver (VQE) [25][26][27] and the Quantum Approximate Optimization Algorithm (QAOA) [28][29][30]. They are more fault-tolerant than quantum algorithms of the standard paradigm and are promising for Noisy Intermediate-Scale Quantum (NISQ) technology [31].Generally, when applying quantum algorithms to solve a classical NP (non-deterministic polynomial-time) problem, we are given a quantum "problem Hamiltonian" (oracle) whose ground state is the solution of the original classical problem [21,[32][33][34]. Designing a quantum algorithm is equivalent to find a "driving Hamiltonian" and an initial state, both easily implemented, that can steer the initial state to the target state (e.g., ground state of the problem Hamiltonian) within the shortest time. From this point of view, time-optimal control [35-37] appears to be fundamentally connected to quantum computation as both a...

A formalism based on Pontryagin's maximum principle is applied to determine the time-optimal protocol that drives a general initial state to a target state by a Hamiltonian with limited control, i.e., there is a single control field with bounded amplitude. The coupling between the bath and the qubit is modeled by a Lindblad master equation. Dissipation typically drives the system to the maximally mixed state, consequently there generally exists an optimal evolution time beyond which the decoherence prevents the system from getting closer to the target state. For some specific dissipation channel, however, the optimal control can keep the system from the maximum entropy state for infinitely long. The conditions under which this specific situation arises are discussed in detail. The numerical procedure to construct the time-optimal protocol is described. In particular, the formalism adopted here can efficiently evaluate the time-dependent singular control which turns out to be crucial in controlling either an isolated or a dissipative qubit.

Spectrogram representations of acoustic scenes have achieved competitive performance for acoustic scene classification. Yet, the spectrogram alone does not take into account a substantial amount of time-frequency information. In this study, we present an approach for exploring the benefits of deep scalogram representations, extracted in segments from an audio stream. The approach presented firstly transforms the segmented acoustic scenes into bump and morse scalograms, as well as spectrograms; secondly, the spectrograms or scalograms are sent into pre-trained convolutional neural networks; thirdly, the features extracted from a subsequent fully connected layer are fed into (bidirectional) gated recurrent neural networks, which are followed by a single highway layer and a softmax layer; finally, predictions from these three systems are fused by a margin sampling value strategy. We then evaluate the proposed approach using the acoustic scene classification data set of 2017 IEEE AASP Challenge on Detection and Classification of Acoustic Scenes and Events (DCASE). On the evaluation set, an accuracy of 64.0 % % % from bidirectional gated recurrent neural networks is obtained when fusing the spectrogram and the bump scalogram, which is an improvement on the 61.0 % % % baseline result provided by the DCASE 2017 organisers. This result shows that extracted bump scalograms are capable of improving the classification accuracy, when fusing with a spectrogram-based system.