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Quantum partial search for uneven distribution of multiple target items

  • Stony Brook University

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Quantum partial search algorithm is an approximate search. It aims to find a target block (which has the target items). It runs a little faster than full Grover search. In this paper, we consider quantum partial search algorithm for multiple target items unevenly distributed in a database (target blocks have different number of target items). The algorithm we describe can locate one of the target blocks. Efficiency of the algorithm is measured by number of queries to the oracle. We optimize the algorithm in order to improve efficiency. By perturbation method, we find that the algorithm runs the fastest when target items are evenly distributed in database.

Original languageEnglish
Article number143
JournalQuantum Information Processing
Volume17
Issue number6
DOIs
StatePublished - Jun 1 2018

Keywords

  • Approximate search
  • Database search
  • Grover search
  • Multiple targets
  • Optimization
  • Quantum algorithm

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