The geometry of quantum learning

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dc.creatorHunziker, Markus
dc.creatorMeyer, David A.
dc.creatorPark, Jihun
dc.creatorPommersheim, James
dc.creatorRothstein, Mitch
dc.date2003-09-05
dc.date.accessioned2026-07-07T06:07:46Z
dc.date.available2026-07-07T06:07:46Z
dc.descriptionConcept 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.
dc.description20 pages, plain TeX with amssym.tex, related work at http://www.math.uga.edu/~hunziker/ and http://math.ucsd.edu/~dmeyer/
dc.identifierhttps://arxiv.org/abs/quant-ph/0309059
dc.identifierhttp://arxiv.org/abs/quant-ph/0309059
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/91411
dc.subjectQuantum Physics
dc.titleThe geometry of quantum learning
dc.typetext

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