The geometry of quantum learning
Quantum Physics
2007-05-23 v1
Abstract
Concept learning provides a natural framework in which to place the problems solved by the quantum algorithms of Bernstein-Vazirani and Grover. By combining the tools used in these algorithms--quantum fast transforms and amplitude amplification--with a novel (in this context) tool--a solution method for geometrical optimization problems--we derive a general technique for quantum concept learning. We name this technique "Amplified Impatient Learning" and apply it to construct quantum algorithms solving two new problems: BATTLESHIP and MAJORITY, more efficiently than is possible classically.
Cite
@article{arxiv.quant-ph/0309059,
title = {The geometry of quantum learning},
author = {Markus Hunziker and David A. Meyer and Jihun Park and James Pommersheim and Mitch Rothstein},
journal= {arXiv preprint arXiv:quant-ph/0309059},
year = {2007}
}
Comments
20 pages, plain TeX with amssym.tex, related work at http://www.math.uga.edu/~hunziker/ and http://math.ucsd.edu/~dmeyer/