.png){kind=icon}
.png){kind=icon}
Return
GraMA: A Gradient Matrix-Guided Assignment Method for Solving Qubit Mapping Problems
.png){kind=icon}
OA
AI
DOI：10.1016/j.future.2026.108485.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• This work proposes a practical initial layout method that utilizes matrix formulation to determine enhanced qubit assignments, without iterative optimization techniques and combinatorial search. • The proposed method computes the gradient of the objective function through a single matrix differentiation to obtain assignment suitability information between logical and physical qubits. • The gradient matrix encodes the interaction between physical-qubit centrality and logical-qubit connectivity, enabling direct mapping decisions that minimize error costs while satisfying hardware constraints. • Simulation results show that the proposed method improves computational efficiency while maintaining comparable execution reliability compared to existing qubit mapping methods.
Keywords:
Quantum Computing
Quantum Compilation
Qubit Mapping
Quadratic Assignment Problem
Matrix Formulation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
F
IF:
0
Papers:
642
Citations:
0
Organization
No organization information available