Abstract 

We demonstrate how quantum computation can provide non-trivial improvements in the computational and statistical complexity of the perceptron model. We develop two quantum algorithms for perceptron learning. The first algorithm exploits quantum information processing to determine a separating hyperplane  using a number of steps sublinear in the number of data points N , namely . The second algorithm illustrates how the classical mistake bound of  can be further improved to  through quantum means, where denotes the margin. Such improvements are achieved through the application of quantum amplitude amplification to the version space interpretation of the perceptron model.