用强化学习优化离子量子计算机的离子搬运，效率提升超三成。
Reinforcement learning for ion shuttling on trapped-ion quantum computers
- 用强化学习直接学习离子搬运策略，适应复杂芯片结构。
- 相比现有方法，搬运操作减少最多达36.3%。
- 适用于多种芯片设计，助力未来大规模量子计算架构。
可扩展的离子阱量子计算通常采用模块化芯片，包含存储、制备和门操作等不同功能区域。执行量子电路需将离子在这些区域间运输，称为离子搬运。随着离子数量增加，搬运优化成为高维难题，传统方法难以高效求解。本文首次将强化学习（RL）应用于离子搬运优化，利用与环境交互自主学习最优策略。实验表明，该方法优于当前最先进的启发式技术，搬运操作最多减少36.3%。此外，该方法可适配多种芯片架构，为芯片设计阶段研究搬运效率提供有力工具，对未来发展更复杂的量子系统具有重要意义。
原文摘要 · Abstract (English)
Scalable trapped-ion quantum computing is commonly realized with modular chips that feature distinct zones with specific functionalities, such as storage, state preparation, and gate execution. To execute a quantum circuit, the ions must be transported between these zones. This process is called ion shuttling. To achieve reliable computation results, the shuttling process must be optimized. However, as the number of ions increases, this becomes a high-dimensional optimization problem where optimal solutions cannot be computed efficiently. We demonstrate, to the best of our knowledge, the first use of reinforcement learning (RL) for the optimization of ion shuttling. RL is well-suited for such scenarios, as it enables learning a strategy through direct interaction with the problem. We show that our RL approach outperforms current state-of-the-art heuristic techniques, yielding a reduction in shuttling operations of up to 36.3 %. Furthermore, we show that our method is easily applicable to various chip architectures. Our approach offers a versatile method to study shuttling efficiency during chip design and, therefore, a highly relevant tool for future, more complex architectures.
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