Two new results about quantum exact learning
We present two new results about exact learning by quantum computers. First, we show how to exactly learn a $k$-Fourier-sparse $n$-bit Boolean function from $O(k^{1.5}(\log k)^2)$ uniform quantum examples for that function. This improves over the bound of $\widetilde{\Theta}(kn)$ uniformly random $c...
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Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2021
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oai:doaj.org-article:8200064c8301448c9fed6779fd3e55be2021-11-24T14:44:09ZTwo new results about quantum exact learning2521-327X10.22331/q-2021-11-24-587https://doaj.org/article/8200064c8301448c9fed6779fd3e55be2021-11-01T00:00:00Zhttps://quantum-journal.org/papers/q-2021-11-24-587/pdf/https://doaj.org/toc/2521-327XWe present two new results about exact learning by quantum computers. First, we show how to exactly learn a $k$-Fourier-sparse $n$-bit Boolean function from $O(k^{1.5}(\log k)^2)$ uniform quantum examples for that function. This improves over the bound of $\widetilde{\Theta}(kn)$ uniformly random $classical$ examples (Haviv and Regev, CCC'15). Additionally, we provide a possible direction to improve our $\widetilde{O}(k^{1.5})$ upper bound by proving an improvement of Chang's lemma for $k$-Fourier-sparse Boolean functions. Second, we show that if a concept class $\mathcal{C}$ can be exactly learned using $Q$ quantum membership queries, then it can also be learned using $O\left(\frac{Q^2}{\log Q}\log|\mathcal{C}|\right)$ $classical$ membership queries. This improves the previous-best simulation result (Servedio and Gortler, SICOMP'04) by a $\log Q$-factor.Srinivasan ArunachalamSourav ChakrabortyTroy LeeManaswi ParaasharRonald de WolfVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenarticlePhysicsQC1-999ENQuantum, Vol 5, p 587 (2021) |
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Physics QC1-999 Srinivasan Arunachalam Sourav Chakraborty Troy Lee Manaswi Paraashar Ronald de Wolf Two new results about quantum exact learning |
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We present two new results about exact learning by quantum computers. First, we show how to exactly learn a $k$-Fourier-sparse $n$-bit Boolean function from $O(k^{1.5}(\log k)^2)$ uniform quantum examples for that function. This improves over the bound of $\widetilde{\Theta}(kn)$ uniformly random $classical$ examples (Haviv and Regev, CCC'15). Additionally, we provide a possible direction to improve our $\widetilde{O}(k^{1.5})$ upper bound by proving an improvement of Chang's lemma for $k$-Fourier-sparse Boolean functions. Second, we show that if a concept class $\mathcal{C}$ can be exactly learned using $Q$ quantum membership queries, then it can also be learned using $O\left(\frac{Q^2}{\log Q}\log|\mathcal{C}|\right)$ $classical$ membership queries. This improves the previous-best simulation result (Servedio and Gortler, SICOMP'04) by a $\log Q$-factor. |
format |
article |
author |
Srinivasan Arunachalam Sourav Chakraborty Troy Lee Manaswi Paraashar Ronald de Wolf |
author_facet |
Srinivasan Arunachalam Sourav Chakraborty Troy Lee Manaswi Paraashar Ronald de Wolf |
author_sort |
Srinivasan Arunachalam |
title |
Two new results about quantum exact learning |
title_short |
Two new results about quantum exact learning |
title_full |
Two new results about quantum exact learning |
title_fullStr |
Two new results about quantum exact learning |
title_full_unstemmed |
Two new results about quantum exact learning |
title_sort |
two new results about quantum exact learning |
publisher |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
publishDate |
2021 |
url |
https://doaj.org/article/8200064c8301448c9fed6779fd3e55be |
work_keys_str_mv |
AT srinivasanarunachalam twonewresultsaboutquantumexactlearning AT souravchakraborty twonewresultsaboutquantumexactlearning AT troylee twonewresultsaboutquantumexactlearning AT manaswiparaashar twonewresultsaboutquantumexactlearning AT ronalddewolf twonewresultsaboutquantumexactlearning |
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1718415000075239424 |