AlphaClifford：基于模型强化学习的高效Clifford电路综合与 transpilation
AlphaClifford: Efficient Clifford Synthesis and Transpilation with Model-based RL
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中文总结 AI 辅助
本研究提出基于模型强化学习的AlphaClifford框架，用于高效综合Clifford电路，在总门数和CNOT门数上优于现有方法，还可用于硬件约束 transpilation及Clifford+T流程优化，为量子编译提供可扩展途径。
中文摘要 AI 辅助
Clifford电路在量子计算中具有基础性作用，尤其在量子纠错和容错逻辑综合方面至关重要。尽管这类电路可被高效模拟并表示为辛矩阵，但诸如Aaronson-Gottesman算法等标准综合方法通常会产生门数过高的次优电路。本研究中，我们提出AlphaClifford，这是一个基于蒙特卡洛树搜索的模型强化学习框架，旨在从由H、S和CNOT组成的基本门集合中高效综合Clifford电路。通过利用辛群的代数性质对状态空间进行建模，AlphaClifford能够有效探索该组合空间以最小化整体电路代价。在无约束Clifford优化任务中，尽管AlphaClifford使用的门集合表达能力严格弱于现有方法，但其在总门数和两量子比特（CNOT）门数上均实现了与最先进综合启发式方法相比的持续减少。此外，我们在另外两项任务上验证了该框架的广泛适用性：在硬件约束的Clifford transpilation任务中，其性能优于现有的基于强化学习的编译器；以及作为完整Clifford+T逻辑综合流程中的后综合优化组件。我们的结果表明，基于模型的强化学习在解决量子编译的组合复杂性方面非常有效，为缓解近期及未来容错量子设备的硬件约束提供了可扩展的途径。
英文摘要
Clifford circuits play a foundational role in quantum computing, particularly due to their importance in quantum error correction and fault-tolerant logical synthesis. While these circuits can be efficiently simulated and represented as symplectic matrices, standard synthesis methods-such as the Aaronson-Gottesman algorithm-often yield sub-optimal circuits with excessively high gate counts. In this work, we introduce AlphaClifford, a model-based Reinforcement Learning framework powered by Monte Carlo Tree Search, designed to efficiently synthesize Clifford circuits from the fundamental gate set composed of H, S, and CNOT. By modeling the state space through the algebraic properties of the symplectic group, AlphaClifford effectively explores this combinatorial space to minimize overall circuit cost. For unconstrained Clifford optimization, our approach achieves a consistent reduction in both total and two-qubit (CNOT) gate counts compared to state-of-the-art synthesis heuristics, despite operating with a strictly less expressive gate set. Furthermore, we demonstrate the broad applicability of our framework on two additional tasks: hardware-constrained Clifford transpilation, where we outperform existing RL-based compilers, and as a post-synthesis optimization component within a full Clifford+T logical synthesis pipeline. Our results underscore that model-based RL is highly effective at addressing the combinatorial complexities of quantum compilation, offering a scalable pathway to mitigate hardware constraints in both near-term and future fault-tolerant quantum devices.
发表机构
- University of Udine（乌迪内大学）
- University of Naples “Federico II”（那不勒斯费德里科二世大学）
机构由 AI 辅助整理，请以论文原文为准。
