The entanglement classification of quantum states is nearly related to the positive homogeneous function that is invariant under stochastic local operations and classical communication (SLOCC) transformations. In this paper, first we give a general scheme to measure the N-tangle as an entanglement measure of an arbitrary odd-N qubit pure state. Then we develop a classification algorithm that is based on the integration between COMPETITIVE LEARNING and the computational power of quantum computing. Using this algorithm the entanglement measure we classify an input into one of two binary classes even if the input pattern is incomplete. The entanglement quantification and classification of quantum states of a large number of particles becomes quickly complex. This major challenge was raised within the quantum information science regarding the physical phenomenon of entanglement as a key resource. During the past years, the polynomial invariants such as concurrence for two-qubit states and three-tangle for pure three-qubit states was defined.

Background and Objectives: A predefined structure is usually employed for deep neural networks, which results in over- or underfitting, heavy processing load, and storage overhead. Training along with pruning can decrease redundancy in deep neural networks; however, it may lead to a decrease in accuracy.Methods: In this note, we provide a novel approach for structure optimization of deep neural networks based on competition of connections merged with brain-inspired synaptic pruning. The efficiency of each network connection is continuously assessed in the proposed scheme based on the global gradient magnitude criterion, which also considers positive scores for strong and more effective connections and negative scores for weak connections. But a connection with a weak score is not removed quickly; instead, it is eliminated when its net score reaches a predetermined threshold. Moreover, the pruning rate is obtained distinctly for each layer of the network.Results: Applying the suggested algorithm to a neural network model of a distillation column in a noisy environment demonstrates its effectiveness and applicability.Conclusion: The proposed method, which is inspired by connection competition and synaptic pruning in the human brain, enhances LEARNING speed, preserves accuracy, and reduces costs due to its smaller network size. It also handles noisy data more efficiently by continuously assessing network connections

Competition law is a newcomer to the legal system recently. A sound understanding of competition policy can provide us with sufficient bases to apply a fundamental and normative view of the issues of competition law. The difference in supervision and regulation determines how the market functions and in order to understand this difference one must understand competition policy. Competition policy may be based on governmental support for national production and industry or on a non-interventional and regulatory posture. Moreover, supervision, based on the principle of non-intervention in the market mechanism, is rooted in liberal ideas; however, regulation, whether as a rule or an exception, is based on the assertion that the market has been ineffective in attaining its goals. Therefore, the government will resort to interventions to regulate inefficiencies.  This paper aims to analyze Supervisory Authority in Implementing COMPETITIVE Policy by employing the description method. In this article the author tries to first delineate competition policy, its related requirements and imposed deviations to the market. Then, by defining the supervisory entity and clarifying its distinction from the regulatory institutions, the author considers the characteristics of an appropriate supervisory entity conducting a comparative study of this issue in Iran and the U.S.A. This form of Competition policy because of its applicable experiences which have been well described by recent scholarship is considered suitable for the native system.