Detection of Entangled States Supported by Reinforcement Learning

Discrimination of entangled states is an important element of quantum-enhanced metrology. This typically requires low-noise detection technology. Such a challenge can be circumvented by introducing nonlinear readout process. Traditionally, this is realized by reversing the very dynamics that generates the entangled state, which requires a full control over the system evolution. In this Letter, we present nonlinear readout of highly entangled states by employing reinforcement learning to manipulate the spin-mixing dynamics in a spin-1 atomic condensate. The reinforcement learning found results in driving the system toward an unstable fixed point, whereby the (to be sensed) phase perturbation is amplified by the subsequent spin-mixing dynamics. Working with a condensate of 10 900 87Rb atoms, we achieve a metrological gain of 6.97 thorn 1.30 -1.38 dB beyond the classical precision limit. Our work will open up new possibilities in unlocking the full potential of entanglement caused quantum enhancement in experiments.

Automated summary

Discrimination of entangled states is crucial in quantum-enhanced metrology, often requiring low-noise detection technology. This challenge can be overcome by introducing a nonlinear readout process. In this study, reinforcement learning is used to manipulate the spin-mixing dynamics in a spin-1 atomic condensate to achieve nonlinear readout of highly entangled states.