About this document
Q-MAML - Quantum Model-Agnostic Meta-Learning For Variational Quantum Algorithms by 徐育兆 is a document available to read on EtoBox.
The document presents Q-MAML, a quantum-classical hybrid framework designed to optimize parameterized quantum circuits (PQCs) for variational quantum algorithms (VQAs) by leveraging Model-Agnostic Meta-Learning (MAML) techniques. This approach aims to improve parameter initialization, ensuring faster convergence and adaptability across various Hamiltonian optimization problems. Experimental results demonstrate that Q-MAML effectively addresses challenges such as barren plateaus, enhancing the efficiency of
- Author
- 徐育兆
- Language
- EN