Have a personal or library account? Click to login
Width Optimization of Quantum Circuit Based on Reuse-Aimed Quantum Circuit Transformation Cover

Width Optimization of Quantum Circuit Based on Reuse-Aimed Quantum Circuit Transformation

Open Access
|Jul 2025

Abstract

Due to constraints in current quantum hardware manufacturing, the number of available qubits remains limited. This limitation poses significant challenges to the feasibility of quantum computation. Qubit reuse, supported by the hardware-enabled dynamic quantum circuit, has emerged as a promising method for optimizing the width of quantum circuits. However, enabling width optimization through qubit reuse for circuits that lack qubit reuse opportunities remains a challenging problem. This paper aims to address this challenge by introducing RaQCT (Reuseaimed Quantum Circuit Transformation), an algorithm for converting non-reusable qubit pairs into reusable ones. Moreover, this paper proposes a more general method for identifying reusable qubits based on the perspective of the execution sequence of the quantum gates. Based on the proposed identification method of reusable qubit pairs and RaQCT, this paper proposes width optimization strategies based on a tree structure. Compared with state-of-the-art qubit reuse methods, our proposed methods enable further circuit width optimization, which proves effective. Furthermore, the comprehensive set of methods proposed in this paper for handling qubit reuse does not require specific differentiation of circuit types. Meanwhile, it is applicable to both static and dynamic circuits, thereby also demonstrating generality.

DOI: https://doi.org/10.2478/qic-2025-0011 | Journal eISSN: 3106-0544 | Journal ISSN: 1533-7146
Language: English
Page range: 216 - 231
Submitted on: Mar 7, 2025
Accepted on: Apr 16, 2025
Published on: Jul 1, 2025
Published by: Cerebration Science Publishing Co., Limited
In partnership with: Paradigm Publishing Services
Publication frequency: 1 issue per year
Related subjects:

© 2025 Haotian Tang, Fei Ding, Xueyun Cheng, Shuxian Zhao, Zhijin Guan, published by Cerebration Science Publishing Co., Limited
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.