| 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
- Environment Definition: Model the trapped-ion chip as a state space with distinct zones (storage, preparation, gate execution) and the ion positions.
- RL Agent Design: Define actions as ion shuttling operations between adjacent zones; reward function optimizes for minimal shuttling operations while maintaining correctness.
- Training: Train RL agent through interaction with the chip simulation environment.
- 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
