A transformer-based reinforcement learning agent learns to reconfigure atoms in neutral atom arrays and reduces estimated logarithmic infidelity by up to about 20% on benchmark circuits, including unseen ones.
Here, we assume that half the atoms are moved per step ϵ = 0 .5
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Quantum circuits as a game: A reinforcement learning agent for quantum compilation and its application to reconfigurable neutral atom arrays
A transformer-based reinforcement learning agent learns to reconfigure atoms in neutral atom arrays and reduces estimated logarithmic infidelity by up to about 20% on benchmark circuits, including unseen ones.