Reinforcement-Learned Electric-Field Sensing with Asymmetrically Blockaded Rydberg Arrays
We present a reinforcement learning-optimized Rydberg electrometer based on the asymmetric blockade effect and achieve high-sensitivity electric field sensing in Rydberg arrays. Microwave dressing induces asymmetric blockade to suppress interactions between target atoms, while keeping the coupling between the central control atom and target atoms field-tunable near Förster resonance. The field-regulated blockade radius affects the detectable atomic population signals, thereby enabling electric field sensing via state-selective readout. In planar atomic arrays, classical Fisher information exhibits near-quadratic scaling with atom number and approaches the Heisenberg limit. Reinforcement learning-designed composite pulses greatly enhance quantum Fisher information by up to one order of magnitude compared with single $π$ pulses. We further establish a compact six-atom spherical configuration for vector electrometry, in which field orientation is extracted from calibrated axial populations, and weak bias fields eliminate dipole-dipole-induced sign and magic-angle ambiguities. Numerical tests against Rabi frequency deviation, positional error, residual inter-target coupling and projection noise demonstrate the reliability of this scheme. This work provides an experimentally viable approach to realize high-precision three-dimensional Rydberg electric field sensing.