Reinforcement learning for ion shuttling on trapped-ion quantum computers

TL;DR

This paper introduces a reinforcement learning approach to optimize ion shuttling in trapped-ion quantum computers, significantly reducing shuttling operations and adaptable to various chip architectures.

Contribution

First application of reinforcement learning for ion shuttling optimization, outperforming heuristic methods and applicable to diverse quantum chip designs.

Findings

RL reduces shuttling operations by up to 36.3%.

RL outperforms current heuristic techniques.

Method is adaptable to various chip architectures.

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.