用强化学习统一合成任意拓扑的量子置换电路
AI Methods for Permutation Circuit Synthesis Across Generic Topologies
- 基于通用矩形晶格训练模型，用掩码动态适配不同拓扑
- 25量子比特下接近最优，且无需重新训练即可跨拓扑使用
- 可微调提升特定拓扑性能，适合实际量子编译流程
本文研究了在通用拓扑上实现置换电路综合与转译的人工智能方法。采用强化学习技术，实现了最多25量子比特的近似最优电路合成。不针对单一拓扑设计专用模型，而是先在通用矩形晶格上训练基础模型，并通过掩码机制在合成时动态选择拓扑子集，使模型能适用于所有可嵌入该晶格的拓扑，无需重训练。本文展示了5×5晶格的结果，与以往面向拓扑的AI模型和经典启发式方法对比，表明其优于经典方法，达到专用AI模型水平，并可处理训练中未见的拓扑。此外，模型可通过微调增强特定拓扑的性能。该方法使得单一训练模型能高效支持多种拓扑的电路合成，具备集成到实际转译工作流的潜力。
原文摘要 · Abstract (English)
This paper investigates artificial intelligence (AI) methodologies for the synthesis and transpilation of permutation circuits across generic topologies. Our approach uses Reinforcement Learning (RL) techniques to achieve near-optimal synthesis of permutation circuits up to 25 qubits. Rather than developing specialized models for individual topologies, we train a foundational model on a generic rectangular lattice, and employ masking mechanisms to dynamically select subsets of topologies during the synthesis. This enables the synthesis of permutation circuits on any topology that can be embedded within the rectangular lattice, without the need to re-train the model. In this paper we show results for 5x5 lattice and compare them to previous AI topology-oriented models and classical methods, showing that they outperform classical heuristics, and match previous specialized AI models, and performs synthesis even for topologies that were not seen during training. We further show that the model can be fine tuned to strengthen the performance for selected topologies of interest. This methodology allows a single trained model to efficiently synthesize circuits across diverse topologies, allowing its practical integration into transpilation workflows.
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