  Computers has been endowed with a part of human-like intelligence owing to
the rapid development of the artificial intelligence technology represented by
the neural networks. Facing the challenge to make machines more imaginative, we
consider a quantum stochastic neural network (QSNN), and propose a learning
algorithm to update the parameters governing the network evolution. The QSNN
can be applied to a class of classification problems, we investigate its
performance in sentence classification and find that the coherent part of the
quantum evolution can accelerate training, and improve the accuracy of verses
recognition which can be deemed as a quantum enhanced associative memory. In
addition, the coherent QSNN is found more robust against both label noise and
device noise so that it is a more adaptive option for practical implementation.
