Reinforcement learning optimizes ion shuttling on trapped-ion quantum chips and reduces operations by up to 36.3% versus heuristics across multiple architectures.
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quant-ph 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
A superconducting quantum router enables programmable multi-qubit entangling operations, demonstrated with faster preparation of entangled states and RL-trained 2- and 3-qubit gates like Toffoli and Fredkin.
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Reinforcement learning for ion shuttling on trapped-ion quantum computers
Reinforcement learning optimizes ion shuttling on trapped-ion quantum chips and reduces operations by up to 36.3% versus heuristics across multiple architectures.
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Efficient $n$-qubit entangling operations via a superconducting quantum router
A superconducting quantum router enables programmable multi-qubit entangling operations, demonstrated with faster preparation of entangled states and RL-trained 2- and 3-qubit gates like Toffoli and Fredkin.