Abstract
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.
Highlights
Over the last few decades, many researchers and publications have been dedicated to improve the performance of neural networks
In order to fully simulate biological neuronal information processing mechanisms and to enhance the approximation and generalization ability of artificial neural network (ANN), we proposed a qubit neural network model with sequence input based on controlled-rotation gates, called quantum-inspired neural network with sequence input (QNNSI)
This paper proposes a quantum-inspired neural network model with sequence input based on the principle of quantum computing
Summary
Over the last few decades, many researchers and publications have been dedicated to improve the performance of neural networks. Purushothaman et al [12] proposed the model of quantum neural network with multilevel hidden neurons based on the superposition of quantum states in the quantum theory. In our previous work [17], we proposed a quantum BP neural network model with learning algorithm based on the single-qubit rotation gates and twoqubits controlled-rotation gates. In order to fully simulate biological neuronal information processing mechanisms and to enhance the approximation and generalization ability of ANN, we proposed a qubit neural network model with sequence input based on controlled-rotation gates, called QNNSI. Owing to the normalization condition, the qubit’s state can be represented by a point on a sphere of unit radius, called the Bloch Sphere.
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