Improved Quantum Query Complexity on Easier Inputs
Abstract
Quantum span program algorithms for function evaluation sometimes have reduced query complexity when promised that the input has a certain structure. We design a modified span program algorithm to show these improvements persist even without a promise ahead of time, and we extend this approach to the more general problem of state conversion. As an application, we prove exponential and superpolynomial quantum advantages in average query complexity for several search problems, generalizing Montanaro's Search with Advice [Montanaro, TQC 2010].
Cite
@article{arxiv.2303.00217,
title = {Improved Quantum Query Complexity on Easier Inputs},
author = {Noel T. Anderson and Jay-U Chung and Shelby Kimmel and Da-Yeon Koh and Xiaohan Ye},
journal= {arXiv preprint arXiv:2303.00217},
year = {2024}
}
Comments
v2) New explicit description and analysis of distributions leading to average quantum advantages, accepted to Quantum. v1) 35 pages, 2 figures. This article supersedes arXiv/2012.01276 (expanded author list, new application, improved algorithm)