The speed of quantum and classical learning for performing the kth root of NOT
Manzano Diosdado, Daniel
Pawłowski, Marcin
Brukner, Časlav
Quantum computation
We consider quantum learning machines—quantum computers that modify themselves in order to improve their performance in some way—that are trained to perform certain classical task, i.e. to execute a function that takes classical bits as input and returns classical bits as output. This allows a fair comparison between learning efficiency of quantum and classical learning machines in terms of the number of iterations required for completion of learning. We find an explicit example of the task for which numerical simulations show that quantum learning is faster than its classical counterpart. The task is extraction of the kth root of NOT (NOT = logical negation), with k=2m and... The reason for this speed-up is that the classical machine requires memory of size log k=m to accomplish the learning, while the memory of a single qubit is sufficient for the quantum machine for any k.
2014-09-19T11:20:57Z
2014-09-19T11:20:57Z
2009
info:eu-repo/semantics/article
Manzano, D.; Pawłowski, M.; Brukner, C. The speed of quantum and classical learning for performing the kth root of NOT . New Journal of Physics, 11: 113018 (2009). [http://hdl.handle.net/10481/33097]
1367-2630
http://hdl.handle.net/10481/33097
10.1088/1367-2630/11/11/113018
eng
http://creativecommons.org/licenses/by-nc-nd/3.0/
info:eu-repo/semantics/openAccess
Creative Commons Attribution-NonCommercial-NoDerivs 3.0 License
IOP Publishing