Work

Optimizing quantum transformation matrices: Block decomposition approach for efficient gate reduction

Year: 2025

Type: article

Abstract: This paper introduces an algorithm designed to approximate quantum transformation matrix with a restricted number of gates by using the block decomposition technique. Addressing challenges posed by numerous gates in handling large qubit transformations, the algorithm provides a solution by optimizing gate usage while maintaining computational accuracy. Inspired by the block decompose algorithm, our approach processes transformation matrices in a blockwise manner, enabling users to specify the desired gate count for flexibility in resource allocation. Simulations validate the effectiveness of the algorithm in approximating transformations with significantly fewer gates, enhancing quantum computing efficiency for complex calculations.

Source: Physical Review A

DOI: 10.1103/physreva.111.042613

Authors: Kin Man Lai, Xin Wang

Institutions: City University of Hong Kong, City University of Hong Kong, Shenzhen Research Institute, Quantum Science Center of Guangdong-Hong Kong-Macao Greater Bay Area

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Shenzhen Fundamental Research Program

Awards: G4198312213, G576520228, G5922047612, 应用机器学习方法对量子点自旋量子比特抗噪调控的理论研究