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trapped-ion quantum computing
Trends
- 1Reinforcement learning improves trapped-ion quantum computing▼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. Machine learning was used to optimize the transport of ions, a key operation for scaling up this leading quantum computing platform. The results were published in Physical Review Research, with physicists highlighting the promise of AI methods for controlling quantum hardware.
- 2Neutral atoms lead the quantum qubit race, says Hassinger▼Sebastian Hassinger (The New Quantum Era): why neutral atoms lead the qubit race for now
Sebastian Hassinger, host of The New Quantum Era podcast, argues that neutral-atom platforms currently hold the lead among quantum computing qubit technologies. He points to the approach's combination of scalability, long coherence times and flexible connectivity as reasons it is outpacing rival architectures such as superconducting and trapped-ion systems, at least for now.