Hybrid Quantum-inspired Neural Networks Model and Algorithm

To enhance the mapping ability of artificial neural networks, by studying the mapping mechanism of hidden layer neurons, a new idea of designing neural networks model based on rotation of qubits in the Bloch sphere is proposed in this paper. In the proposed approach, the samples are linearly transformed to quantum bit phase, and the qubits are rotated about three coordinate axes, respectively. The network parameters of hidden layer are the rotation angles. The spherical coordinates of qubits can be obtained by the projection measurement. The output of hidden layer neurons can be concluded by submitting these coordinates to excitation functions in hidden layer. The general neurons are applied to the output layer. The learning algorithms of the proposed model are designed based on the Levenberg-Marquardt (L-M) algorithm. The experimental results show that the proposed model is superior to the classical (L-M) algorthm in approximation ability, generalization ability, and robust performance.