This paper considers the teleportation of quantum controlled-Not （CNOT） gate by using partially entangled states. Different from the known probability schemes, it presents a method for teleporting a CNOT gate with u...This paper considers the teleportation of quantum controlled-Not （CNOT） gate by using partially entangled states. Different from the known probability schemes, it presents a method for teleporting a CNOT gate with unit fidelity and unit probability by using two partially entangled pairs as quantum channel. The method is applicable to any two partially entangled pairs satisfying the condition that their smaller Schmidt coefficients μ and ν are （2μ ＋ 2ν - 2μν - 1） ≥ 0. In this scheme, the sender＇s local generalized measurement described by a positive operator valued measurement （POVM） lies at the heart. It constructs the required POVM. It also puts forward a scheme for teleporting a CNOT with two targets gate with unit fidelity by using same quantum channel. With assistance of local operations and classical communications, three spatially separated users are able to complete the teleportation of a CNOT with two targets gate with probability of （2μ ＋ 2ν - 1）. With a proper value of μ and ν, the probability could reach nearly 1.展开更多
吸收塔内浆液的pH是影响燃煤电厂湿法脱硫系统效率的重要参数。燃煤电厂的湿法脱硫系统具有大滞后、非线性、强耦合等特征,因而其吸收塔浆液的pH很难实现精准控制。利用门控循环单元(gated recurrent unit,GRU)神经网络在处理时间序列...吸收塔内浆液的pH是影响燃煤电厂湿法脱硫系统效率的重要参数。燃煤电厂的湿法脱硫系统具有大滞后、非线性、强耦合等特征,因而其吸收塔浆液的pH很难实现精准控制。利用门控循环单元(gated recurrent unit,GRU)神经网络在处理时间序列数据的优越性,对吸收塔内的浆液pH进行预测建模,通过将燃煤电厂采集的影响浆液pH的变量数据作为模型的输入,对模型进行训练处理,获得吸收塔内浆液pH的预测模型。将预测模型应用于辽宁省华能营口电厂600 MW机组湿法脱硫智能控制系统中吸收塔内浆液pH的预测。结果表明相比于反向传播(back propagation,BP)神经网络模型、径向基函数(radial basis function,RBF)神经网络、循环神经网络(recurrent neural network,RNN)和长短期记忆(long and short term memory,LSTM)神经网络,该模型精确度更高,实用性更强。展开更多
基金Project supported by the Natural Science Foundation of Guangdong Province,China (Grant No 06029431)
文摘This paper considers the teleportation of quantum controlled-Not （CNOT） gate by using partially entangled states. Different from the known probability schemes, it presents a method for teleporting a CNOT gate with unit fidelity and unit probability by using two partially entangled pairs as quantum channel. The method is applicable to any two partially entangled pairs satisfying the condition that their smaller Schmidt coefficients μ and ν are （2μ ＋ 2ν - 2μν - 1） ≥ 0. In this scheme, the sender＇s local generalized measurement described by a positive operator valued measurement （POVM） lies at the heart. It constructs the required POVM. It also puts forward a scheme for teleporting a CNOT with two targets gate with unit fidelity by using same quantum channel. With assistance of local operations and classical communications, three spatially separated users are able to complete the teleportation of a CNOT with two targets gate with probability of （2μ ＋ 2ν - 1）. With a proper value of μ and ν, the probability could reach nearly 1.
文摘吸收塔内浆液的pH是影响燃煤电厂湿法脱硫系统效率的重要参数。燃煤电厂的湿法脱硫系统具有大滞后、非线性、强耦合等特征,因而其吸收塔浆液的pH很难实现精准控制。利用门控循环单元(gated recurrent unit,GRU)神经网络在处理时间序列数据的优越性,对吸收塔内的浆液pH进行预测建模,通过将燃煤电厂采集的影响浆液pH的变量数据作为模型的输入,对模型进行训练处理,获得吸收塔内浆液pH的预测模型。将预测模型应用于辽宁省华能营口电厂600 MW机组湿法脱硫智能控制系统中吸收塔内浆液pH的预测。结果表明相比于反向传播(back propagation,BP)神经网络模型、径向基函数(radial basis function,RBF)神经网络、循环神经网络(recurrent neural network,RNN)和长短期记忆(long and short term memory,LSTM)神经网络,该模型精确度更高,实用性更强。
