Necessary and sufficient conditions for local creation of quantum correlation

Quantum correlation can be created by a local operation from some initially classical states. We prove that the necessary and sufficient condition for a local trace-preserving channel to create quantum correlation is that it is not a commutativity-preserving channel. This condition is valid for arbitrary finite dimension systems. We also derive the explicit form of commutativity-preserving channels. For a qubit, a commutativity-preserving channel is either a completely decohering channel or a mixing channel. For a three-dimensional system (qutrit), a commutativity-preserving channel is either a completely decohering channel or an isotropic channel.Comment: Theorem 2 has been modifie

Graph Reinforcement Learning Application to Co-operative Decision-Making in Mixed Autonomy Traffic: Framework, Survey, and Challenges

Proper functioning of connected and automated vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaboration decision-making for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) has been widely used in solving decision-making problems. However, the existing DRL-based methods have been mainly focused on solving the decision-making of a single CAV. Using the existing DRL-based methods in mixed autonomy traffic cannot accurately represent the mutual effects of vehicles and model dynamic traffic environments. To address these shortcomings, this article proposes a graph reinforcement learning (GRL) approach for multi-agent decision-making of CAVs in mixed autonomy traffic. First, a generic and modular GRL framework is designed. Then, a systematic review of DRL and GRL methods is presented, focusing on the problems addressed in recent research. Moreover, a comparative study on different GRL methods is further proposed based on the designed framework to verify the effectiveness of GRL methods. Results show that the GRL methods can well optimize the performance of multi-agent decision-making for CAVs in mixed autonomy traffic compared to the DRL methods. Finally, challenges and future research directions are summarized. This study can provide a valuable research reference for solving the multi-agent decision-making problems of CAVs in mixed autonomy traffic and can promote the implementation of GRL-based methods into intelligent transportation systems. The source code of our work can be found at https://github.com/Jacklinkk/Graph_CAVs.Comment: 22 pages, 7 figures, 10 tables. Currently under review at IEEE Transactions on Intelligent Transportation System

Quantum correlating power of local quantum channels

We define quantum-correlating power (QCP) of a local quantum channel acting on the left part of a bipartite quantum system as the maximum amount of left quantum correlation that can be created by this channel. We prove that for any local channel, the optimal input state, which corresponds to the maximum quantum correlation in the output state, must be a classical-classical state. Further, the single-qubit channels with maximum QCP can be found in the class of channels which take their optimal input states to rank-two quantum-classical states. A superactivation property of QCP, that is, two zero-QCP channels can constitute a positive-QCP channel, is observed and discussed for single-qubit phase damping channels. The analytic expression for QCP of single-qubit amplitude damping channel is obtained

Green supply chain integration, supply chain agility and green innovation performance: Evidence from Chinese manufacturing enterprises

Despite widespread attention on the significance of green supply chain integration (GSCI), there is still limited research on how GSCI can improve firms’ green innovation performance. From the perspective of the natural resource-based view and dynamic capability theory, based on the theoretical logic of “resource-capability-performance”, this study aims to explore the relationship between GSCI and firms’ green innovation performance and its intrinsic mechanism. In order to test the research model, this study collected survey data from 405 Chinese manufacturing firms and tested them by using hierarchical regression and bootstrap analysis. The results show that all three dimensions of GSCI, namely, green internal integration, green supplier integration, and green customer integration, have positive effects on supply chain agility. In addition, supply chain agility has a significant positive impact on green product and process innovation. This study also finds that supply chain agility plays a partially mediating role between all three dimensions of GSCI and green product and process innovation; that is, GSCI can further promote firms’ green innovation performance by improving supply chain agility. The results of this study not only enrich the theoretical research on the driving factors of firms’ green innovation but also provide policy implications for manufacturing firms and government policy-makers regarding the implementation and promotion of green innovation practices