Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Log inSign up
Challenges
Datasets
Workspaces
Papers
Discussions
Leaderboard
Blog
Job Board
Q3AS

© 2026 Aqora Quantum S.A.S.

TermsPrivacyLegal Notice

Lecture notes in computer science

Average-Case Quantum Query Complexity

Andris Ambainis, Ronald de Wolf

Published Jan 1, 2000• 4 citations
Publisher
DOI
10.1007/3-540-46541-3_11

Related papers

Average/Worst-Case Gap of Quantum Query Complexities (アルゴリズム(AL) Vol.2009-AL-124)

Andris Ambainis, Kazuo Iwama, Masaki Nakanishi +4 more

研究報告アルゴリズム(AL)•May 4, 2009•0 citations

Average-case query complexity of fixed-weight boolean functions and k-CNFs

Yuan Li, Mingjun Liu, Haowei Wu +1 more

Theoretical Computer Science•Aug 8, 2026•0 citations

Query Complexity: Worst-Case Quantum Versus Average-Case Classical

Scott Aaronson

In this note we investigate the relationship between worst-case quantum query complexity and average-case classical query complexity. Specifically, we show that if a quantum computer can evaluate a total Boolean function f with bounded error using T queries in the worst case, then a deterministic classical computer can evaluate f using O(T^5) queries in the average case, under a uniform distribution of inputs. If f is monotone, we show furthermore that only O(T^3) queries are needed. Previously, Beals et al. (1998) showed that if a quantum computer can evaluate f with bounded error using T queries in the worst case, then a deterministic classical computer can evaluate f using O(T^6) queries in the worst case, or O(T^4) if f is monotone. The optimal bound is conjectured to be O(T^2), but improving on O(T^6) remains an open problem. Relating worst-case quantum complexity to average-case classical complexity may suggest new ways to reduce the polynomial gap in the ordinary worst-case versus worst-case setting.

arXiv:cs/0001013•Jan 19, 2000•0 citations

Nonadaptive quantum query complexity

Ashley Montanaro

Information Processing Letters•Sep 22, 2010•14 citations

Average/Worst-Case Gap of Quantum Query Complexities by On-Set Size

Andris Ambainis, Kazuo Iwama, Masaki Nakanishi +4 more

This paper considers the query complexity of the functions in the family F_{N,M} of N-variable Boolean functions with onset size M, i.e., the number of inputs for which the function value is 1, where 1<= M <= 2^{N}/2 is assumed without loss of generality because of the symmetry of function values, 0 and 1. Our main results are as follows: (1) There is a super-linear gap between the average-case and worst-case quantum query complexities over F_{N,M} for a certain range of M. (2) There is no super-linear gap between the average-case and worst-case randomized query complexities over F_{N,M} for every M. (3) For every M bounded by a polynomial in N, any function in F_{N,M} has quantum query complexity Theta (sqrt{N}). (4) For every M=O(2^{cN}) with an arbitrary large constant c<1, any function in F_{N,M} has randomized query complexity Omega (N).

arXiv:0908.2468•Aug 18, 2009•7 citations

Quantum query complexity, with advice

Can H. Bostanci

•Sep 16, 2026•0 citations
In this note we investigate the relationship between worst-case quantum query complexity and average-case classical query complexity. Specifically, we show that if a quantum computer can evaluate a total Boolean function f with bounded error using T queries in the worst case, then a deterministic classical computer can evaluate f using O(T^5) queries in the average case, under a uniform distribution of inputs. If f is monotone, we show furthermore that only O(T^3) queries are needed. Previously, Beals et al. (1998) showed that if a quantum computer can evaluate f with bounded error using T queries in the worst case, then a deterministic classical computer can evaluate f using O(T^6) queries in the worst case, or O(T^4) if f is monotone. The optimal bound is conjectured to be O(T^2), but improving on O(T^6) remains an open problem. Relating worst-case quantum complexity to average-case classical complexity may suggest new ways to reduce the polynomial gap in the ordinary worst-case versus worst-case setting.
This paper considers the query complexity of the functions in the family F_{N,M} of N-variable Boolean functions with onset size M, i.e., the number of inputs for which the function value is 1, where 1<= M <= 2^{N}/2 is assumed without loss of generality because of the symmetry of function values, 0 and 1. Our main results are as follows: (1) There is a super-linear gap between the average-case and worst-case quantum query complexities over F_{N,M} for a certain range of M. (2) There is no super-linear gap between the average-case and worst-case randomized query complexities over F_{N,M} for every M. (3) For every M bounded by a polynomial in N, any function in F_{N,M} has quantum query complexity Theta (sqrt{N}). (4) For every M=O(2^{cN}) with an arbitrary large constant c<1, any function in F_{N,M} has randomized query complexity Omega (N).