Reinforcement learning for ion shuttling on trapped-ion quantum computers
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 ...
| Authors | Maximilian Schier, Lea Richtmann, Christian Staufenbiel et al. |
|---|---|
| Primary category | quant-ph |
| Published | 21 May 2026 |
| Last updated at source | 21 May 2026 |
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