Quantum Algorithms for Learning and Testing Juntas
 Alp Atıcı,
 Rocco A. Servedio
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Get AccessIn this article we develop quantum algorithms for learning and testing juntas, i.e. Boolean functions which depend only on an unknown set of k out of n input variables. Our aim is to develop efficient algorithms: (1) whose sample complexity has no dependence on n, the dimension of the domain the Boolean functions are defined over; (2) with no access to any classical or quantum membership (“blackbox”) queries. Instead, our algorithms use only classical examples generated uniformly at random and fixed quantum superpositions of such classical examples; (3) which require only a few quantum examples but possibly many classical random examples (which are considered quite “cheap” relative to quantum examples). Our quantum algorithms are based on a subroutine FS which enables sampling according to the Fourier spectrum of f; the FS subroutine was used in earlier work of Bshouty and Jackson on quantum learning. Our results are as follows: (1) We give an algorithm for testing kjuntas to accuracy ε that uses O(k/ϵ) quantum examples. This improves on the number of examples used by the best known classical algorithm. (2) We establish the following lower bound: any FSbased kjunta testing algorithm requires \(\Omega(\sqrt{k})\) queries. (3) We give an algorithm for learning kjuntas to accuracy ϵ that uses O(ϵ^{−1} k log k) quantum examples and O(2^{ k } log(1/ϵ)) random examples. We show that this learning algorithm is close to optimal by giving a related lower bound.
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 Title
 Quantum Algorithms for Learning and Testing Juntas
 Journal

Quantum Information Processing
Volume 6, Issue 5 , pp 323348
 Cover Date
 20071001
 DOI
 10.1007/s1112800700616
 Print ISSN
 15700755
 Online ISSN
 15731332
 Publisher
 Springer US
 Additional Links
 Topics
 Keywords

 Juntas
 quantum query algorithms
 quantum property testing
 computational learning theory
 quantum computation
 lower bounds
 03.67.a
 03.67.Lx
 Industry Sectors
 Authors

 Alp Atıcı ^{(1)}
 Rocco A. Servedio ^{(2)}
 Author Affiliations

 1. Citadel Investment Group, Chicago, IL, 60603, USA
 2. Department of Computer Science, Columbia University, New York, NY, 10027, USA