摘要
提出一种量子神经网络模型及算法.该模型为一组量子门线路.输入信息用量子位表示,经量子旋转门进行相位旋转后作为控制位,控制隐层量子位的翻转;隐层量子位经量子旋转门进行相位旋转后作为控制位,控制输出层量子位的翻转.以输出层量子位中激发态的概率幅作为网络输出,基于梯度下降法构造了该模型的学习算法.仿真结果表明,该模型及算法在收敛能力和鲁棒性方面均优于普通BP网络.
A quantum neural networks model and its algorithm are proposed. The model is a group of the quantum gate circuits. The input information is expressed as the qubits, which, as the control qubits after rotated by the rotation gate, control the qubits in hidden layer to reversal. The qubits in hidden layer, as the control qubits after rotated by the rotation gate, control the qubits in output layer to reversal. The final output is described by the probability amplitudes of excited states in output layer. The learning algorithm is presented based on the gradient descent algorithm. The simulation results show that the proposed algorithm is superior to the common BP neural networks in both convergence capability and robustness.
出处
《控制与决策》
EI
CSCD
北大核心
2012年第1期143-146,151,共5页
Control and Decision
基金
国家自然科学基金项目(61170132)
中国博士后科学基金特别项目(201003405)
中国博士后科学基金项目(20090460864)
黑龙江省博士后科学基金项目(LBH-Z09289)
黑龙江省教育厅科学技术研究项目(11551015)
关键词
量子计算
量子旋转门
受控非门
量子线路
量子神经网络
quantum computing
quantum rotation gate
controlled-not gate
quantum circuit
quantum neural networks
