• DOI:10.1007/s00037-015-0099-2
• Corpus ID: 1605779

Quantum Algorithms for Learning Symmetric Juntas via the Adversary Bound

@article{Belovs2014QuantumAF,
  title={Quantum Algorithms for Learning Symmetric Juntas via the Adversary Bound},
  author={Aleksandrs Belovs},
  journal={computational complexity},
  year={2014},
  volume={24},
  pages={255-293}
}

• Aleksandrs Belovs
• Published 26 November 2013
• Computer Science, Mathematics
• computational complexity

In this paper, we study the following variant of the junta learning problem. We are given oracle access to a Boolean function f on n variables that only depends on k variables, and, when restricted to them, equals some predefined function h. The task is to identify the variables the function depends on.When h is the XOR or the OR function, this gives a restricted variant of the Bernstein–Vazirani or the combinatorial group testing problem, respectively.We analyze the general case using the adversary bound and give an alternative formulation for the quantum query complexity of this problem. We construct optimal quantum query algorithms for the cases when h is the OR function (complexity is $${\Theta(\sqrt{k})}$$Θ(k)) or the exact-half function (complexity is $${\Theta(k^{1/4})}$$Θ(k1/4)). The first algorithm resolves an open problem from Ambainis & Montanaro (Quantum Inf Comput 14(5&6): 439–453, 2014). For the case when h is the majority function, we prove an upper bound of $${O(k^{1/4})}$$O(k1/4). All these algorithms can be made exact. We obtain a quartic improvement when compared to the randomized complexity (if h is the exact-half or the majority function), and a quadratic one when compared to the non-adaptive quantum complexity (for all functions considered in the paper).