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.
From that subset, iden- tify all “active” columns and rows Ac = [ q {c(t) q , c(t+1) q }, A r = [ q {r(t) q , r(t+1) q } in that rearrangement
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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.