arXiv:2605.22463v1 Announce Type: cross 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 de
The increasing complexity of trapped-ion quantum computers necessitates advanced optimization techniques, and reinforcement learning offers a promising approach to overcome current scaling challenges.
Optimizing ion shuttling is critical for developing scalable and reliable trapped-ion quantum computers, directly impacting the viability and performance of a foundational quantum computing paradigm.
The application of reinforcement learning could significantly improve the efficiency and reliability of quantum operations by automating and optimizing complex ion transport, potentially accelerating quantum computer development.
- · Quantum computing researchers
- · Quantum hardware manufacturers
- · AI/ML algorithm developers
- · Traditional optimization methods
Increased performance and qubit count in trapped-ion quantum computers due to more efficient ion management.
Accelerated development of practical quantum applications as quantum hardware becomes more stable and scalable.
Potential for trapped-ion systems to gain a competitive advantage in the race to build fault-tolerant quantum computers.
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