dorsal/arxiv
View SchemaThe geometry of quantum learning
| Authors | Markus Hunziker, David A. Meyer, Jihun Park, James Pommersheim, Mitch Rothstein |
|---|---|
| Categories | |
| ArXiv ID | quant-ph/0309059 |
| URL | https://arxiv.org/abs/quant-ph/0309059 |
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.
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"abstract": "Concept learning provides a natural framework in which to place the problems\nsolved by the quantum algorithms of Bernstein-Vazirani and Grover. By combining\nthe tools used in these algorithms--quantum fast transforms and amplitude\namplification--with a novel (in this context) tool--a solution method for\ngeometrical optimization problems--we derive a general technique for quantum\nconcept learning. We name this technique \"Amplified Impatient Learning\" and\napply it to construct quantum algorithms solving two new problems: BATTLESHIP\nand MAJORITY, more efficiently than is possible classically.",
"arxiv_id": "quant-ph/0309059",
"authors": [
"Markus Hunziker",
"David A. Meyer",
"Jihun Park",
"James Pommersheim",
"Mitch Rothstein"
],
"categories": [
"quant-ph"
],
"title": "The geometry of quantum learning",
"url": "https://arxiv.org/abs/quant-ph/0309059"
},
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"execution_id": "a940eee1-c5c3-471c-acd6-6e300ca7c342",
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