Meta-Learning for Quantum Optimization via Quantum Sequence Model

• URL: http://arxiv.org/abs/2512.05058v1
• Date: Thu, 04 Dec 2025 18:13:45 GMT
• Title: Meta-Learning for Quantum Optimization via Quantum Sequence Model
• Authors: Yu-Cheng Lin, Yu-Chao Hsu, Samuel Yen-Chi Chen,
• Abstract summary: We show that QK-LSTM achieves superior performance, obtaining the highest approximation ratios and exhibiting the fastest rate of learning to learn to 13.<n>This, underscores the power of the kernel and underscores its effectiveness.
• Score: 14.766654275662589
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The Quantum Approximate Optimization Algorithm (QAOA) is a leading approach for solving combinatorial optimization problems on near-term quantum processors. However, finding good variational parameters remains a significant challenge due to the non-convex energy landscape, often resulting in slow convergence and poor solution quality. In this work, we propose a quantum meta-learning framework that trains advanced quantum sequence models to generate effective parameter initialization policies. We investigate four classical or quantum sequence models, including the Quantum Kernel-based Long Short-Term Memory (QK-LSTM), as learned optimizers in a "learning to learn" paradigm. Our numerical experiments on the Max-Cut problem demonstrate that the QK-LSTM optimizer achieves superior performance, obtaining the highest approximation ratios and exhibiting the fastest convergence rate across all tested problem sizes (n=10 to 13). Crucially, the QK-LSTM model achieves perfect parameter transferability by synthesizing a single, fixed set of near-optimal parameters, leading to a remarkable sustained acceleration of convergence even when generalizing to larger problems. This capability, enabled by the compact and expressive power of the quantum kernel architecture, underscores its effectiveness. The QK-LSTM, with only 43 trainable parameters, substantially outperforms the classical LSTM (56 parameters) and other quantum sequence models, establishing a robust pathway toward highly efficient parameter initialization for variational quantum algorithms in the NISQ era.

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