Quantcast

Extreme Spin Squeezing from Deep Reinforcement Learning

Research paper by Feng Chen, Jun-Jie Chen, Ling-Na Wu, Yong-Chun Liu, Li You

Indexed on: 08 Jan '21Published on: 27 Sep '19Published in: arXiv - Condensed Matter - Quantum Gases



Abstract

Spin squeezing (SS) is a recognized resource for realizing measurement precision beyond the standard quantum limit $\propto 1/\sqrt{N}$. 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 Heisenberg limit $\propto 1/{N}$. Aided by deep reinforcement learning (DRL), we discover size-independent universal rules for realizing nearly extreme SS with OAT interaction using merely a handful of rotation pulses. More specifically, only 6 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 study highlights the potential of DRL for controlled quantum dynamics.