Work

Bi-Quadratic Improvement in Conditional Quantum Search

Year: 2023

Type: preprint

Abstract: The Grover search algorithm performs an unstructured search of a marked item in a database quadratically faster than classical algorithms and is shown to be optimal. Here, we show that if the search space is divided into two blocks with the local query operators and the global operators satisfy certain condition, then it is possible to achieve an improvement of bi-quadratic speed-up. Furthermore, we investigate the bi-quadratic speed-up in the presence of noise and show that it can tolerate noisy scenario. This may have potential applications for diverse fields, including database searching, and optimization, where efficient search algorithms play a pivotal role in solving complex computational problems.

Source: arXiv (Cornell University)

DOI: 10.48550/arxiv.2312.05680

Authors: Dash, Akankshya, Panda, Biswaranjan, Pati, Arun K

Institutions: KIIT University, Indian Institute of Science Education and Research Berhampur, International Institute of Information Technology, Hyderabad

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: International Institute of Information Technology, Hyderabad

OpenAlex ID: W4389650513

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