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Reinforcement learning improves trapped-ion quantum computing

Original: Machine learning optimizes trapped-ion quantum computing – Reinforcement learning beats state-of-the-art techniques for

Researchers at the Max Planck Institute for Gravitational Physics report that reinforcement learning outperforms state-of-the-art techniques for shuttling ions in trapped-ion quantum computers. The machine learning approach optimizes how ions are moved within the hardware, a key bottleneck for scaling these systems. The findings are described in a Physical Review Research paper.

Why now: A new study shows machine learning beating established control methods in a leading quantum computing platform.

Max Planck Institute for Gravitational PhysicsPhysical Review Researchtrapped-ion quantum computing

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