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 $O(sqrt{N})$. The second algorithm illustrates how the classical mistake bound of $O(frac{1}{gamma^2})$ can be further improved to $O(frac{1}{sqrt{gamma}})$ through quantum means, where $gamma$ denotes the margin. Such improvements are achieved through the application of quantum amplitude amplification to the version space interpretation of the perceptron model.
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