Hierarchical and probabilistic quantum state sharing via a non-maximally entangled |χ〉state

A scheme that probabilistically realizes hierarchical quantum state sharing of an arbitrary unknown qubit state with a four-qubit non-maximally entangled |χ state is presented in this paper. In the scheme, the sender Alice distributes a quantum secret with a Bell-state measurement and publishes her measurement outcomes via a classical channel to three agents who are divided into two grades. One agent is in the upper grade, while the other two agents are in the lower grade. Then by introducing an ancillary qubit, the agent of the upper grade only needs the assistance of any one of the other two agents for probabilistically obtaining the secret, while an agent of the lower grade needs the help of both the other two agents by using a controlled-NOT operation and a proper positive operator-valued measurement instead of the usual projective measurement. In other words, the agents of two different grades have different authorities to reconstruct Alice's secret in a probabilistic manner. The scheme can also be modified to implement the threshold-controlled teleportation.

Modeling and TOPSIS-GRA Algorithm for Autonomous Driving Decision-Making Under 5G-V2X Infrastructure

This paper is to explore the problems of intelligent connected vehicles(ICVs)autonomous driving decision-making under a 5G-V2X structured road environment.Through literature review and interviews with autonomous driving practitioners,this paper firstly puts forward a logical framework for designing a cerebrum-like autonomous driving system.Secondly,situated on this framework,it builds a hierarchical finite state machine(HFSM)model as well as a TOPSIS-GRA algorithm for making ICV autonomous driving decisions by employing a data fusion approach between the entropy weight method(EWM)and analytic hierarchy process method(AHP)and by employing a model fusion approach between the technique for order preference by similarity to an ideal solution(TOPSIS)and grey relational analysis(GRA).The HFSM model is composed of two layers:the global FSM model and the local FSM model.The decision of the former acts as partial input information of the latter and the result of the latter is sent forward to the local pathplanning module,meanwhile pulsating feedback to the former as real-time refresh data.To identify different traffic scenarios in a cerebrum-like way,the global FSM model is designed as 7 driving behavior states and 17 driving characteristic events,and the local FSM model is designed as 16 states and 8 characteristic events.In respect to designing a cerebrum-like algorithm for state transition,this paper firstly fuses AHP weight and EWM weight at their output layer to generate a synthetic weight coefficient for each characteristic event;then,it further fuses TOPSIS method and GRA method at the model building layer to obtain the implementable order of state transition.To verify the feasibility,reliability,and safety of theHFSMmodel aswell as its TOPSISGRA state transition algorithm,this paper elaborates on a series of simulative experiments conducted on the PreScan8.50 platform.The results display that the accuracy of obstacle detection gets 98%,lane line prediction is beyond 70 m,the speed of collision avoidance is higher than 45 km/h,the distance of collision avoidance is less than 5 m,path planning time for obstacle avoidance is averagely less than 50 ms,and brake deceleration is controlled under 6 m/s2.These technical indexes support that the driving states set and characteristic events set for the HFSM model as well as its TOPSIS-GRA algorithm may bring about cerebrum-like decision-making effectiveness for ICV autonomous driving under 5G-V2X intelligent road infrastructure.

TABS: Transforming automatically BPMN models into blockchain smart contracts

Research on blockchains addresses multiple issues,with one being the automated creation of smart contracts.Developing smart contract methods is more difficult than mainstream software development as the underlying blockchain infrastructure poses additional complexity.We report on a new approach to developing smart contracts with the objective of automating the process to increase developer efficiency and reduce the risk of errors introduced by software developers.To support industry adoption,we use Business Process Model and Notation(BPMN)modeling to describe an application while targeting applications in the trade vertical.We describe a system that transforms a BPMN model into a multi-modal model that combines Discrete Event(DE)modeling for concurrency with Hierarchical State Machines(HSMs)to represent application functionality.Then,further transformations are used to transform the DE-HSM model into methods in smart contracts.The system lets the modeler decide which of the independent patterns should be transformed into methods of a separate smart contract that is deployed on a sidechain for the purpose of(i)reducing processing costs and/or(ii)providing privacy so that other participants in the smart contract do not have visibility into the processing of the pattern.We also briefly describe a proof-of-concept tool we built to demonstrate the feasibility of our approach.