Quantum Superposition Spiking Neural Network
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Quantum brain as a novel hypothesis states that some non-trivial mechanisms in quantum computation, such as superposition and entanglement, may have important influence for the formation of brain functions. Inspired by this idea, we propose Quantum Superposition Spiking Neural Network (QS-SNN), which introduce quantum superposition to spiking neural network models to handel challenges which are hard for other state-of-the-art machine learning models. For human brain, grasping the main information no matter how the background changes is necessary to interact efficiently with diverse environments. As an example, it is easy for human to recognize the digits whether it is white character with black background or inversely black character with white background. While if the current machine learning models are trained with one of the cases (e.g. white character with black background), it will be nearly impossible for them to recognize the color inverted version. To handel this challenge, we propose two-compartment spiking neural network with superposition states encoding, which is inspired by quantum information theory and spatial-temporal spiking property from neuron information encoding in the brain. Typical network structures like fully-connected ANN, VGG, ResNet and DenseNet are challenged with the same task. We train these networks on original image dataset and then invert the background color to test their generalization. Result shows that artificial neural network can not deal with this condition while the quantum superposition spiking neural network(QS-SNN) which we proposed in this paper recognizes the color-inverse image successfully. Further the QS-SNN shows its robustness when noises are added on inputs.
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