A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is...A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.展开更多
为进一步提高量子神经网络的性能,结合目前神经网络机理的研究进展,提出了一种基于量子门组的量子神经元模型,建立了量子门组量子神经网络(Quantum Gate Set Neural Network,QGSNN)。该算法由输入层、隐含层和输出层组成,该算法将转换...为进一步提高量子神经网络的性能,结合目前神经网络机理的研究进展,提出了一种基于量子门组的量子神经元模型,建立了量子门组量子神经网络(Quantum Gate Set Neural Network,QGSNN)。该算法由输入层、隐含层和输出层组成,该算法将转换后的量子态训练样本作为输入。利用量子旋转门和通用量子门完成旋转、选择、翻转和聚合等一系列操作,并完成了网络参数的更新。将训练后的结果输出。QGSNN算法的泛化能力在数学上得到了证明,并利用两个仿真实验对该方法进行验证。实验结果表明,与普通神经网络和普通量子神经网络相比,QGSNN算法在泛化性能、鲁棒性、准确率和执行时间等方面具有较好的效果。展开更多
基金the National Natural Science Foundation of China (50138010)
文摘A quantum BP neural networks model with learning algorithm is proposed. First, based on the universality of single qubit rotation gate and two-qubit controlled-NOT gate, a quantum neuron model is constructed, which is composed of input, phase rotation, aggregation, reversal rotation and output. In this model, the input is described by qubits, and the output is given by the probability of the state in which （1） is observed. The phase rotation and the reversal rotation are performed by the universal quantum gates. Secondly, the quantum BP neural networks model is constructed, in which the output layer and the hide layer are quantum neurons. With the application of the gradient descent algorithm, a learning algorithm of the model is proposed, and the continuity of the model is proved. It is shown that this model and algorithm are superior to the conventional BP networks in three aspects： convergence speed, convergence rate and robustness, by two application examples of pattern recognition and function approximation.
文摘为进一步提高量子神经网络的性能,结合目前神经网络机理的研究进展,提出了一种基于量子门组的量子神经元模型,建立了量子门组量子神经网络(Quantum Gate Set Neural Network,QGSNN)。该算法由输入层、隐含层和输出层组成,该算法将转换后的量子态训练样本作为输入。利用量子旋转门和通用量子门完成旋转、选择、翻转和聚合等一系列操作,并完成了网络参数的更新。将训练后的结果输出。QGSNN算法的泛化能力在数学上得到了证明,并利用两个仿真实验对该方法进行验证。实验结果表明,与普通神经网络和普通量子神经网络相比,QGSNN算法在泛化性能、鲁棒性、准确率和执行时间等方面具有较好的效果。
