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Extreme spin squeezing from deep reinforcement learning

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Spin squeezing (SS) is a recognized resource for realizing measurement precision beyond the standard quantum limit $\ensuremath{\propto}1/\sqrt{N}$. The rudimentary one-axis twisting (OAT) interaction can facilitate SS and has been realized… Click to show full abstract

Spin squeezing (SS) is a recognized resource for realizing measurement precision beyond the standard quantum limit $\ensuremath{\propto}1/\sqrt{N}$. The rudimentary one-axis twisting (OAT) interaction can facilitate SS and has been realized in diverse experiments, but it cannot achieve extreme SS for precision at the Heisenberg limit $\ensuremath{\propto}1/N$. Aided by deep reinforcement learning (DRL), we discover size-independent universal rules for realizing nearly extreme SS with a OAT interaction using merely a handful of rotation pulses. More specifically, only six pairs of pulses are required for up to ${10}^{4}$ particles, while the time taken to reach extreme SS remains on the same order of the optimal OAT squeezing time, which makes our scheme viable for experiments that reported OAT squeezing. This Rapid Communication highlights the potential of DRL for controlled quantum dynamics.

Keywords: extreme spin; reinforcement learning; spin squeezing; deep reinforcement

Journal Title: Physical Review A
Year Published: 2019

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