rl-ion-shuttling-trapped-ion

name	rl-ion-shuttling-trapped-ion
description	Reinforcement learning for ion shuttling on trapped-ion quantum computers — RL-based optimization of ion transport in modular trapped-ion chips, achieving up to 36.3% reduction in shuttling operations.

RL for Ion Shuttling on Trapped-Ion Quantum Computers

Reinforcement learning methodology for optimizing ion shuttling operations in modular trapped-ion quantum computers. Scalable trapped-ion quantum computing is commonly realized with modular chips featuring distinct zones for storage, state preparation, and gate execution. Ion shuttling between these zones is a high-dimensional optimization problem where RL demonstrates significant improvement over heuristic methods.

Key Insights

• First RL application to ion shuttling: Demonstrates the first use of reinforcement learning for optimizing ion transport in trapped-ion architectures.
• 36.3% reduction in shuttling operations: RL approach outperforms current state-of-the-art heuristic techniques with significant reduction in shuttling moves.
• Architecture-agnostic: The method is easily applicable to various chip architectures, providing a versatile tool for chip design exploration.
• RL from direct interaction: RL learns an optimal shuttling strategy through direct interaction with the problem environment.

Methodology

1. Environment Definition: Model the trapped-ion chip as a state space with distinct zones (storage, preparation, gate execution) and the ion positions.
2. RL Agent Design: Define actions as ion shuttling operations between adjacent zones; reward function optimizes for minimal shuttling operations while maintaining correctness.
3. Training: Train RL agent through interaction with the chip simulation environment.
4. Evaluation: Compare shuttling operation count against heuristic baselines across different chip architectures.

Algorithm Details

• State space: Ion positions across chip zones, current circuit stage
• Action space: Ion shuttling moves (single/multi-ion transport between zones)
• Reward: Negative shuttling operations (minimization objective)
• RL Algorithm: Standard DRL approach suitable for combinatorial optimization

Applications

• Trapped-ion quantum computer design and optimization
• Modular chip architecture exploration
• Quantum circuit compilation for trapped-ion platforms
• Co-design of chip architecture and shuttling strategies

References

• Paper: Maximilian Schier, Lea Richtmann, Christian Staufenbiel, Tobias Schmale, Daniel Borcherding, Michèle Heurs, Bodo Rosenhahn. "Reinforcement learning for ion shuttling on trapped-ion quantum computers" (2026)
• arXiv: 2605.22463
• Categories: quant-ph, cs.LG

Activation

• Ion shuttling optimization, trapped-ion quantum computer
• RL for quantum hardware, quantum circuit compilation
• Modular chip architecture, ion transport routing