Quantum amplification and purification of noisy coherent states

Quantum-limited amplifiers increase the amplitude of quantum signals at the price of introducing additional noise. Quantum purification protocols operate in the reverse way, by reducing the noise while attenuating the signal. Here we investigate a scenario that interpolates between these two extremes. We search for the optimal physical process that generates $M$ approximate copies of pure and amplified coherent state, starting from $N$ copies of a noisy coherent state with Gaussian modulation. We prove that the optimal deterministic processes are always Gaussian, whereas non-Gaussianity powers up probabilistic advantages in suitable parameter regimes. The optimal processes are experimentally feasible, both in the deterministic and in the probabilistic scenario. In view of this fact, we provide benchmarks that can be used to certify the experimental demonstration of the quantum-enhanced amplification and purification of coherent states.Comment: 10 page

Quantum Metrology with Indefinite Causal Order

We address the study of quantum metrology enhanced by indefinite causal order, demonstrating a quadratic advantage in the estimation of the product of two average displacements in a continuous variable system. We prove that no setup where the displacements are probed in a fixed order can have root-mean-square error vanishing faster than the Heisenberg limit 1/N, where N is the number of displacements contributing to the average. In stark contrast, we show that a setup that probes the displacements in a superposition of two alternative orders yields a root-mean-square error vanishing with super-Heisenberg scaling 1/N^2. This result opens up the study of new measurement setups where quantum processes are probed in an indefinite order, and suggests enhanced tests of the canonical commutation relations, with potential applications to quantum gravity.Comment: 11 pages, 3 figure

General Agreement on Trade in Services and Higher Education in China

The Chinese higher education system is the largest in the world. Having an understanding of the Chinese system helps to provide a better understanding of international education. This article discusses the commitments China has made under the General Agreement on Trade in Services (GATS), these commitments’ implications for Chinese higher education, and Chinese attitudes towards foreign education. It also discusses the recent development of international cooperation in Chinese higher education. Chinese are interested in learning from developed countries, the demand for higher education continues to grow in China, and most Chinese scholars believe internationalization is beneficial. Indications are Chinese higher educators will expand their cooperation with international colleagues to meet changing social needs. In internationalizing Chinese higher education, GATS may play a facilitating role. L’éducation supérieure chinoise est la plus grande du monde. Comprendre le système chinois aide à comprendre le système international. Cet article discute les accords que la Chine a pris en signant l’Accord Général sur le Commerce des Services (AGCS), les implications pour l’éducation supérieure chinoise et les attitudes chinoises envers l’éducation étrangère. Il expose également les derniers développements de coopération internationale dans l’éducation supérieure chinoise. Les chinois sont intéressés à apprendre des pays développés. La demande d’éducation supérieure continue à croître en Chine et la plupart des intellectuels chinois pensent que l’internationalisation est positive. Les professeurs d’éducation supérieure chinoise vont donc chercher à accroître la coopération avec leurs collègues internationaux afin de répondre aux besoins sociaux actuels. L’AGCS jouera certainement un rôle majeur dans l’internationalisation de l’éducation supérieure de ce pays

Scalable Fair Influence Maximization

Given a graph $G$, a community structure $\mathcal{C}$, and a budget $k$, the fair influence maximization problem aims to select a seed set $S$ ($|S|\leq k$) that maximizes the influence spread while narrowing the influence gap between different communities. While various fairness notions exist, the welfare fairness notion, which balances fairness level and influence spread, has shown promising effectiveness. However, the lack of efficient algorithms for optimizing the welfare fairness objective function restricts its application to small-scale networks with only a few hundred nodes. In this paper, we adopt the objective function of welfare fairness to maximize the exponentially weighted summation over the influenced fraction of all communities. We first introduce an unbiased estimator for the fractional power of the arithmetic mean. Then, by adapting the reverse influence sampling (RIS) approach, we convert the optimization problem to a weighted maximum coverage problem. We also analyze the number of reverse reachable sets needed to approximate the fair influence at a high probability. Further, we present an efficient algorithm that guarantees $1-1/e - \varepsilon$ approximation

Improved Annealing-Genetic Algorithm for Test Case Prioritization

Regression testing, which can improve the quality of software systems, is a useful but time consuming method. Many techniques have been introduced to reduce the time cost of regression testing. Among these techniques, test case prioritization is an effective technique which can reduce the time cost by processing relatively more important test cases at an earlier stage. Previous works have demonstrated that some greedy algorithms are effective for regression test case prioritization. Those algorithms, however, have lower stability and scalability. For this reason, this paper proposes a new regression test case prioritization approach based on the improved Annealing-Genetic algorithm which incorporates Simulated Annealing algorithm and Genetic algorithm to explore a bigger potential solution space for the global optimum. Three Java programs and five C programs were employed to evaluate the performance of the new approach with five former approaches such as Greedy, Additional Greedy, GA, etc. The experimental results showed that the proposed approach has relatively better performance as well as higher stability and scalability than those former approaches

Experimental investigation into rock burst proneness of rock materials considering strain rate and size effect

In deep rock engineering, evaluating the likelihood of rock burst is imperative to ensure safety. This study proposes a new metric, the post-peak dissipated energy index, which accounts for strain rate and size effects in assessment of the rock burst proneness of a rock mass. To investigate rock burst proneness, conventional compression tests were conducted on limestone and slate samples with different length to diameter (L/D) ratios (ranging from 0.3 to 1.5) at four different strain rates (0.005, 0.01, 0.5, and 1.0 s−1). Based on the testing observations, the actual rock burst proneness was classified into three categories (no risk, low risk, and high risk). A new criterion was also established using the post-peak dissipated energy index, which is the ratio of elastic energy to total dissipated energy. The impact of the strain rate and L/D ratio on rock burst proneness was analyzed. The results indicated that increased strain rates cause a strong hardening effect, leading to staged growth of rock burst proneness. However, the rock burst proneness decreases non-linearly with the increasing L/D ratio. The accuracy of the proposed criterion was validated by comparison with existing criteria, demonstrating that the energy-based index ensures a reliable evaluation of the rock burst proneness of a rock mass. The proposed method has excellent potential for practical application in deep rock engineering