Deep reinforcement learning with incremental updates optimizes Rydberg CNOT gates to 0.9991 average fidelity by discovering smooth pulses and an early-cutoff policy.
The dynamics can be simplified by analyzing the target atom’s reduced density matrix ˆρ(00) (for control in |0⟩) and ˆρ(10) (for control in 13 |1⟩)
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Intelligent Optimal Control of Rydberg Gates with Incremental-Update Deep Reinforcement Learning
Deep reinforcement learning with incremental updates optimizes Rydberg CNOT gates to 0.9991 average fidelity by discovering smooth pulses and an early-cutoff policy.