Reinforcement learning for ion shuttling on trapped-ion quantum computers

2026-05-21

AI Summary

This work addresses the high-dimensional optimization challenge in scaling multi-ion shuttling for modular ion-trap quantum chips by introducing reinforcement learning to ion transport control for the first time. The proposed approach learns optimal shuttling policies directly through interaction with the physical system, eliminating reliance on handcrafted heuristics that often lack generalizability. It establishes an efficient and scalable optimization framework applicable across diverse chip architectures. Experimental results demonstrate that the method reduces shuttling operations by up to 36.3% compared to state-of-the-art heuristic techniques and validates its effectiveness and versatility across multiple chip configurations.

Abstract

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.

Problem

• ion shuttling
• trapped-ion quantum computing
• optimization
• scalability
• quantum circuit execution

Innovation

• reinforcement learning
• ion shuttling
• trapped-ion quantum computing
• optimization
• modular quantum architecture