Extreme Spin Squeezing from Deep Reinforcement Learning

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.

5 pages, 4 figures

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• Reinforcement learning of rare diffusive dynamics
• Efficient and Robust Entanglement Generation with Deep Reinforcement Learning for Quantum Metrology
• Heisenberg-limited spin squeezing in coupled spin systems
• Generation and storage of spin squeezing via learning-assisted optimal control
• Active Learning Approach to Optimization of Experimental Control
• Machine Learning for Estimation and Control of Quantum Systems
• Creation and manipulation of quantized vortices in Bose-Einstein condensates using reinforcement learning
• Quantum Metrology via Floquet-Engineered Two-axis Twisting and Turn Dynamics
• Nuclear Spin Squeezing Based on Spin-Exchange Collisions
• Generating large-scale Greenberger-Horne-Zeilinger-like states in lattice spin systems
• Generation of spin-squeezed states using dipole-coupled spins
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