AI 中文总结
该研究提出将CR算法嵌入强化学习环境的框架，在两类门集上实现更短量子电路，训练于小电路的RL可应用于大电路，为量子系统编译优化提供稳健方法。
AI 中文摘要
在含噪声中等规模量子（NISQ）设备上实际实现量子算法时，受真实硬件固有的退相干及其他噪声源影响，存在操作限制。为缓解这些误差同时保留算法原有功能，更短的量子电路更受青睐，这推动了高效量子电路优化算法的开发。基于学习的方法已成为主流候选方案，但现有自主智能体效率较低，其大部分训练能力用于重复发现确定性基于规则的方法已可靠处理的基础化简操作。为解决这一挑战，我们提出一种强化学习框架，将确定性的交换与化简（Commutation-and-Reduction, CR）算法直接嵌入训练环境。在智能体每次动作后，CR算法自动解决基础的交换与抵消问题，使智能体能将学习能力集中于强化学习真正发挥价值的非平凡优化任务。在两种门集（通用Clifford+T基和CNOT+Pauli基）上的实证评估显示，在所有测试规模下，RL+CR生成的电路均比标准RL智能体更短。我们证明，在较小量子电路上训练的RL可应用于更大的量子电路：在20量子比特Clifford+T电路（比训练电路大5倍）上，RL+CR移除的门数量是标准RL的2倍。本研究提供了一种稳健方法，可加速未来容错及实用规模量子系统的编译与优化过程。
英文摘要
The practical implementation of quantum algorithms on noisy intermediate-scale quantum devices encounters operational limitations due to decoherence and other sources of noise inherent in real hardware. To mitigate these errors while preserving the original functionality of the algorithm, shorter quantum circuits are therefore preferred. This motivates the development of effective quantum circuit optimization algorithms. Learning-based approaches have emerged as a leading candidate, yet existing autonomous agents remain inefficient, spending most of their training capacity rediscovering elementary reductions that deterministic rule-based methods already handle reliably. To address this challenge, we propose a reinforcement learning framework that embeds a deterministic Commutation-and-Reduction (CR) algorithm directly into the training environment. After every agent action, the CR algorithm automatically resolves elementary commutations and cancellations, enabling the agent to focus its learning capacity on the non-trivial optimizations where reinforcement learning adds real value. Empirical evaluation across two gate sets, the universal Clifford+T basis and the CNOT+Pauli basis, shows that RL+CR produces shorter circuits than a standard RL agent at all tested scales. We demonstrate that RL trained on smaller quantum circuits can be applied to larger quantum circuits. On 20-qubit Clifford+T circuits, five times larger than the training circuits, RL+CR removes twice as many gates as standard RL. This work provides a robust approach that could accelerate the compilation and optimization processes for future fault-tolerant and utility-scale quantum systems.
Comments11 pages, comments welcome
