mlsanityData to 14 Oct 2026

Reinforcement learning for ion shuttling on trapped-ion quantum computers

arXiv 2605.22463 ↗First posted 21 May 2026quant-ph cs.LGcited by 0

On the map: Deep reinforcement learning methods → Quantum reinforcement learning

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in Quantum reinforcement learning, one of 15 directions in Deep reinforcement learning methods. Builds on 7 of its 41 references that are in the map. First posted on arXiv 21 May 2026.

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7 of 41 references.

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Same direction, newest first

  1. 2026Reinforcement Learning for Quantum Technology26 Jan

Other references · 6

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2015High-Dimensional Continuous Control Using Generalized Advantage Estimationcited by 2,026
  3. 2018IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architecturescited by 883
  4. 2024Reinforcement learningcited by 247
  5. 2024SimBa: Simplicity Bias for Scaling Up Parameters in Deep Reinforcement Learningcited by 76
  6. 2025MUSS-TI: Multi-level Shuttle Scheduling for Large-Scale Entanglement Module Linked Trapped-Ioncited by 2

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  1. 2025Shuttling Compiler for Trapped-Ion Quantum Computers Based on Fine-Tuned Large Language Models
  2. 2024Reinforcement learning with learned gadgets to tackle hard quantum problems on real hardware
  3. 2025Breaking Through Barren Plateaus: Reinforcement Learning Initializations for Deep Variational Quantum Circuits
  4. 2025BenchRL-QAS: Benchmarking reinforcement learning algorithms for quantum architecture search
  5. 2025Separating Ansatz Discovery from Deployment on Larger Problems: Reinforcement Learning for Modular Circuit Design
  6. 2025Hybrid Reward-Driven Reinforcement Learning for Efficient Quantum Circuit Synthesis
  7. 2026SQARL: A Size-Agnostic Reinforcement Learning approach for Circuit Allocation in Distributed Quantum Architectures
  8. 2024Attention-Based Deep Reinforcement Learning for Qubit Allocation in Modular Quantum Architectures