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This article proposes a character-level neural language model (NLM) that is based on quantum theory. The input of the model is the character-level coding represented by the quantum semantic space model. Our model integrates a convolutional neural network (CNN) that is based on network-in-network (NIN). We assessed the effectiveness of our model through extensive experiments based on the English-language Penn Treebank dataset. The experiments results confirm that the quantum semantic inputs work well for the language models. For example, the PPL of our model is 10%–30% less than the states of the arts, while it keeps the relatively smaller number of parameters (i.e., 6 m).