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Main Authors: Anderson, Noel T., Chung, Jay-U, Kimmel, Shelby, Koh, Da-Yeon, Ye, Xiaohan
Format: Preprint
Published: 2023
Subjects:
Online Access:https://arxiv.org/abs/2303.00217
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author Anderson, Noel T.
Chung, Jay-U
Kimmel, Shelby
Koh, Da-Yeon
Ye, Xiaohan
author_facet Anderson, Noel T.
Chung, Jay-U
Kimmel, Shelby
Koh, Da-Yeon
Ye, Xiaohan
contents 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].
format Preprint
id arxiv_https___arxiv_org_abs_2303_00217
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Improved Quantum Query Complexity on Easier Inputs
Anderson, Noel T.
Chung, Jay-U
Kimmel, Shelby
Koh, Da-Yeon
Ye, Xiaohan
Quantum Physics
Data Structures and Algorithms
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].
title Improved Quantum Query Complexity on Easier Inputs
topic Quantum Physics
Data Structures and Algorithms
url https://arxiv.org/abs/2303.00217