Detection of entangled states supported by reinforcement learning

TL;DR

This paper demonstrates a reinforcement learning approach to nonlinear readout of entangled states in a spin-1 atomic condensate, achieving quantum-enhanced measurement precision beyond classical limits.

Contribution

It introduces a novel RL-based method to manipulate spin dynamics for entangled state readout, bypassing the need for full system control.

Findings

Achieved 6.97 dB metrological gain beyond classical limit

Successfully used RL to amplify phase perturbations in spin-1 condensates

Demonstrated practical quantum enhancement in atomic metrology

Abstract

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 work, we present nonlinear readout of highly entangled states by employing reinforcement learning (RL) to manipulate the spin-mixing dynamics in a spin-1 atomic condensate. The RL found results in driving the system towards an unstable fixed point, whereby the (to be sensed) phase perturbation is amplified by the subsequent spin-mixing dynamics. Working with a condensate of 10900 {87}^Rb atoms, we achieve a metrological gain of 6.97 dB beyond the classical precision limit. Our work would open up new possibilities in unlocking the full potential of entanglement caused quantum enhancement in experiments.