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

Quantum Algorithms for Intersection and Proximity Problems

Kunihiko Sadakane, Norito Sugawara, Takeshi Tokuyama

Published Jan 1, 2001• 4 citations
Publisher
DOI
10.1007/3-540-45678-3_14

Related papers

Quantum Algorithms and Applications

Melinda Andrews, Joseph Stephen Dulny, Andrew N. Glaudell +6 more

•Sep 1, 2020•0 citations

Quantum algorithms for intersection and proximity problems

Kunihiko Sadakane, Norito Sugawara, Takeshi Tokuyama

We discuss applications of quantum computation to geometric data processing. Especially, we give efficient algorithms for intersection problems and proximity problems. Our algorithms are based on Brassard et al.'s amplitude amplification method, and analogous to Buhrman et al.'s algorithm for element distinctness. Revealing these applications is useful for classifying geometric problems, and also emphasizing potential usefulness of quantum computation in geometric data processing. Thus, the results will promote research and development of quantum computers and algorithms.

IEICE Transactions on Fundamentals of Electronics Communications and Computer Sciences•May 1, 2003•0 citations

Quantum Algorithm for Jaccard Similarity

Varun Puram, Ruthvik Rao Bobbili, Johnson P. Thomas

Jaccard Similarity is a very common proximity measurement used to compute the similarity between two asymmetric binary vectors. Jaccard Similarity is the ratio between the 1s (Intersection of two vectors) to 1s (Union of two vectors). This paper introduces a quantum algorithm for finding the Jaccard Similarity 1s, in the Intersection and Union of two binary vectors. There are two sub-algorithms one for each. Measuring the register for respective algorithm gives count of number of 1 s in binary format. Implementation on IBM composer is also included.

arXiv:2408.08940•Aug 16, 2024•0 citations

Quantum Algorithms for Matching Problems

Sebastian Dörn

Theory of Computing Systems•May 29, 2008•19 citations

Quantum Algorithm for Finding Sets Intersection

Shengping Tian, Ping Wang, Xiaomei Chen

We propose a quantum algorithm to solve the intersection problem of two sets by extending Bernstein-Vazirani algorithm. Given set and set . In the classic algorithm, we need to query times to find the solution of set . However, in our quantum algorithm, we need only one quantum query to find the set .

International Conference on Intelligent Computing, Automation and Systems (ICICAS)•Dec 1, 2019•0 citations

Quantum Algorithms for Optimization Problems

Dilnoz Muhamediyeva, Rustam Raximov, Shavkat Urokov

Lecture notes in computer science•Jul 29, 2025•0 citations
We discuss applications of quantum computation to geometric data processing. Especially, we give efficient algorithms for intersection problems and proximity problems. Our algorithms are based on Brassard et al.'s amplitude amplification method, and analogous to Buhrman et al.'s algorithm for element distinctness. Revealing these applications is useful for classifying geometric problems, and also emphasizing potential usefulness of quantum computation in geometric data processing. Thus, the results will promote research and development of quantum computers and algorithms.
Jaccard Similarity is a very common proximity measurement used to compute the similarity between two asymmetric binary vectors. Jaccard Similarity is the ratio between the 1s (Intersection of two vectors) to 1s (Union of two vectors). This paper introduces a quantum algorithm for finding the Jaccard Similarity 1s, in the Intersection and Union of two binary vectors. There are two sub-algorithms one for each. Measuring the register for respective algorithm gives count of number of 1 s in binary format. Implementation on IBM composer is also included.