Guest Column-Reference-Cited by-同舟云学术

Guest Column

Published:2017-06-12 Issue:2 Volume:48 Page:41-67
ISSN:0163-5700
Container-title:ACM SIGACT News
language:en
Short-container-title:SIGACT News

Author:

Arunachalam Srinivasan¹,de Wolf Ronald²

Affiliation:

1. QuSoft, CWI, Amsterdam, The Netherlands

2. CWI and University of Amsterdam, The Netherlands

Abstract

This paper surveys quantum learning theory: the theoretical aspects of machine learning using quantum computers. We describe the main results known for three models of learning: exact learning from membership queries, and Probably Approximately Correct (PAC) and agnostic learning from classical or quantum examples.

Publisher

Association for Computing Machinery (ACM)

Link

https://dl.acm.org/doi/pdf/10.1145/3106700.3106710

Reference83 articles.

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2. S. Aaronson. Ten semi-grand challenges for quantum computing theory. http://www.scottaaronson.com/writings/qchallenge.html 2005. S. Aaronson. Ten semi-grand challenges for quantum computing theory. http://www.scottaaronson.com/writings/qchallenge.html 2005.

3. The learnability of quantum states;Aaronson S.;Proceedings of the Royal Society of London,2007

4. Read the fine print

5. M. Anthony and P. Bartlett. Function learning from interpolation. Combinatorics Probability and Computing 9(3):213--225 2000. Earlier version in EuroCOLT'95. 10.1017/S0963548300004247 M. Anthony and P. Bartlett. Function learning from interpolation. Combinatorics Probability and Computing 9(3):213--225 2000. Earlier version in EuroCOLT'95. 10.1017/S0963548300004247

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