arXiv:2512.18021v3 Announce Type: replace-cross Abstract: We present the first shuttling compiler based on large language models (LLMs) for trapped-ion quantum computers, where qubits are shuttled between segments for gate execution and qubit storage. We fine-tune pre-trained LLMs on examples from linear and branched one-dimensional shuttling architectures. Thus, we obtain a layout-independent compilation strategy that learns the required shuttling operations directly from data. Using benchmark circuits with up to 16 qubits, such fine-tuned LLMs can now generate valid schedules for shuttling a
The convergence of advanced large language models and developing quantum computing hardware is enabling new approaches to complex control problems.
This development indicates a significant step towards practical and scalable quantum computing by addressing specific control challenges in trapped-ion architectures.
Quantum computer compilation strategies can now leverage LLMs for optimized qubit shuttling, potentially accelerating the development of more complex quantum systems.
- · Quantum computing hardware developers (trapped-ion)
- · AI/ML research in quantum control
- · High-performance computing (future applications)
- · Traditional quantum compilation methods (less efficient)
- · Quantum architectures less amenable to shuttling optimization
Increased efficiency and scalability of trapped-ion quantum computers due to automated and optimized qubit management.
Faster development and deployment of quantum algorithms and applications, potentially leading to quantum advantage in specific problem domains sooner.
Enhanced competition among different quantum computing modalities as trapped-ion systems gain a competitive edge in control and scalability.
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Read at arXiv cs.LG