@inproceedings{wiebe2016quantum,
author = {Wiebe, Nathan and Kapoor, Ashish and Svore, Krysta M.},
title = {Quantum Perceptron Models},
year = {2016},
month = {February},
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 O(N−−√). The second algorithm illustrates how the classical mistake bound of O(1γ2) can be further improved to O(1γ√) through quantum means, where γ denotes the margin. Such improvements are achieved through the application of quantum amplitude amplification to the version space interpretation of the perceptron model.},
url = {http://approjects.co.za/?big=en-us/research/publication/quantum-perceptron-models/},
pages = {6401},
journal = {30th Conference on Neural Information Processing Systems (NIPS 2016), Barcelona, Spain},
}