Quantum-inspired Neural Network Research Articles

Quantum-inspired Neural Network with Application to Image Restoration

In order to improve the quality of image restoration, a quantum neural network model, whose input of each dimension is a discrete sequence, is proposed. This model concludes three layers, in which the hidden layer consists of quantum neurons, and the output layer consists of classical neurons. The quantum neuron consists of the quantum rotation gates and the multi-qubits controlled-not gates. By using the information feedback of target qubit from output to input in multi-qubits controlled-not gate, the overall memory of input sequences is realized. The output of quantum neuron is obtained from the entanglements of multi-qubits in controlled-not gates. The learning algorithm is designed in detail according to the basis principles of quantum computation. The characteristics of input sequence can be effectively obtained by way of “breadth” and “depth”. The simulation results show that the quality of image restoration of proposed model is obviously superior to that of classical artificial neural network.

Model and algorithm of quantum-inspired neural network with sequence input based on controlled rotation gates

To enhance the approximation and generalization ability of classical artificial neural network (ANN) by employing the principles of quantum computation, a quantum-inspired neuron based on controlled-rotation gate is proposed. In the proposed model, the discrete sequence input is represented by the qubits, which, as the control qubits of the controlled-rotation gate after being rotated by the quantum rotation gates, control the target qubit for rotation. The model output is described by the probability amplitude of state |1〉 in the target qubit. Then a quantum-inspired neural network with sequence input (QNNSI) is designed by employing the quantum-inspired neurons to the hidden layer and the classical neurons to the output layer. An algorithm of QNNSI is derived by employing the Levenberg–Marquardt algorithm. Experimental results of some benchmark problems show that, under a certain condition, the QNNSI is obviously superior to the ANN.

A hybrid quantum-inspired neural networks with sequence inputs

To enhance the performance of classical neural networks, a quantum-inspired neural networks model based on the controlled-Hadamard gates is proposed. In this model, the inputs are discrete sequences described by a matrix where the number of rows is equal to the number of input nodes, and the number of columns is equal to the sequence length. This model includes three layers, in which the hidden layer consists of quantum neurons, and the output layer consists of classical neurons. The quantum neuron consists of the quantum rotation gates and the multi-qubits controlled-Hadamard gates. A learning algorithm is presented in detail according to the basic principles of quantum computation. The characteristics of input sequence can be effectively obtained from both breadth and depth. The experimental results show that, when the number of input nodes is closer to the sequence length, the proposed model is obviously superior to the BP neural networks.