An Examination of Qubit Neural Network in Controlling an Inverted Pendulum
Neural Processing Letters
Model and Training of QNN with Weight
Neural Processing Letters
Quantum probability distribution network
ICIC'07 Proceedings of the intelligent computing 3rd international conference on Advanced intelligent computing theories and applications
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
Remarks on multi-layer quantum neural network controller trained by real-coded genetic algorithm
IScIDE'11 Proceedings of the Second Sino-foreign-interchange conference on Intelligent Science and Intelligent Data Engineering
Adaptive-type servo controller utilizing a quantum neural network with qubit neurons
International Journal of Hybrid Intelligent Systems
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Neural networks have attracted much interest in the last two decades for their potential to realistically describe brain functions, but so far they have failed to provide models that can be simulated in a reasonable time on computers; rather they have been limited to toy models. Quantum computing is a possible candidate for improving the computational efficiency of neural networks. In this framework of quantum computing, the Qubit neuron model, proposed by Matsui and Nishimura, has shown a high efficiency in solving problems such as data compression. Simulations have shown that the Qubit model solves learning problems with significantly improved efficiency as compared to the classical model. In this paper, we confirm our previous results in further detail and investigate what contributes to the efficiency of our model through 4-bit and 6-bit parity check problems, which are known as basic benchmark tests. Our simulations suggest that the improved performance is due to the use of superposition of neural states and the use of probability interpretation in the observation of the output states of the model.