Two extremely rare β-cyclodextrin (β-CD) supported metal-organic frameworks (MOFs), CD-MOF-1 and CD-MOF-2, were induced to crystallize for the first time through a template-induced approach. The targeted CD-MOFs were employed to perform controlled drug delivery and cytotoxicity assays that confirmed their favourable biological potential of being used as drug carriers.

Support vector machine (SVM) is a particularly powerful and flexible supervised learning model that analyze data for both classification and regression, whose usual algorithm complexity scales polynomially with the dimension of data space and the number of data points. Inspired by quantum SVM, we present a quantum-inspired classical algorithm for SVM using fast sampling techniques. In our approach, we developed a method sampling kernel matrix by the given information on data points and make classification through estimation of classification expression. Our approach can be applied to various types of SVM, such as linear SVM, non-linear SVM and soft SVM. Theoretical analysis shows one can make classification with arbitrary success probability in logarithmic runtime of both the dimension of data space and the number of data points, matching the runtime of the quantum SVM.

Synchronization is of great scientific interest due to the abundant applications in a wide range of systems. We propose a scheme to achieve the controllable long-distance synchronization of two dissimilar optomechanical systems, which are unidirectionally coupled through a fiber with light. Synchronization, unsynchronization, and the dependence of the synchronization on driving laser strength and intrinsic frequency mismatch are studied based on the numerical simulation. Taking the fiber attenuation into account, it's shown that two mechanical resonators can be synchronized over a distance of tens of kilometers. In addition, we also analyze the unidirectional synchronization of three optomechanical systems, demonstrating the scalability of our scheme.

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The optimal fixed-point quantum search (OFPQS) algorithm [Phys. Rev. Lett. 113, 210501 (2014)] achieves both the fixed-point property and quadratic speedup over classical algorithms, which gives a sufficient condition on the number of iterations to ensure the success probability is no less than a given lower bound (denoted by 1 − δ 2 ). However, this condition is approximate and not exact. In this paper, we derive the sufficient and necessary condition on the number of feasible iterations, based on which the exact least number of iterations can be obtained. For example, when δ = 0.8, iterations can be saved by almost 25%. Moreover, to find a target item certainly, setting directly 1 − δ 2 = 100%, the quadratic advantage of the OFPQS algorithm will be lost, then, applying the OFPQS algorithm with 1 − δ 2 < 100% requires multiple executions, which leads to a natural problem of choosing the optimal parameter δ. For this, we analyze the extreme and minimum properties of the success probability and further analytically derive the optimal δ which minimizes the query complexity. Our study can be a guideline for both the theoretical and application research on the fixed-point quantum search algorithms.