Detection of entangled states supported by reinforcement learning

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.

References in corpus (47)

• Quantum-enhanced measurements: beating the standard quantum limit
• QuTiP 2: A Python framework for the dynamics of open quantum systems
• Quantum metrology with nonclassical states of atomic ensembles
• Quantum spins mixing in spinor Bose-Einstein condensates
• Dynamical quantum noise in Bose-Einstein condensates
• The truncated Wigner method for Bose condensed gases: limits of validity and applications
• Spin-Nematic Squeezed Vacuum in a Quantum Gas
• Reinforcement Learning in Different Phases of Quantum Control
• Deterministic entanglement generation from driving through quantum phase transitions
• Approaching the Heisenberg limit without single-particle detection
• Resonant control of spin dynamics in ultracold quantum gases by microwave dressing
• Reinforcement Learning with Neural Networks for Quantum Feedback
• Quantum-Enhanced Sensing Based on Time Reversal of Nonlinear Dynamics
• Quantum-enhanced sensing of displacements and electric fields with large trapped-ion crystals
• Quantum amplification of mechanical oscillator motion
• Quantum state magnification
• Experimental quantum speed-up in reinforcement learning agents
• Time-Reversal-Based Quantum Metrology with Many-Body Entangled States
• Optimizing Quantum Error Correction Codes with Reinforcement Learning
• Loschmidt Echo for quantum metrology
• Detecting large quantum Fisher information with finite measurement precision
• Beating the classical precision limit with spin-1 Dicke state of more than 10000 atoms
• Deep Reinforcement Learning for Quantum Gate Control
• Quantum turbulence and correlations in Bose-Einstein condensate collisions
• Topological Quantum Compiling with Reinforcement Learning
• Generalizable control for quantum parameter estimation through reinforcement learning
• Quantum error correction for the toric code using deep reinforcement learning
• Non-equilibrium dynamics of an unstable quantum pendulum
• Global optimization of quantum dynamics with AlphaZero deep exploration
• Parametric amplification of vacuum fluctuations in a spinor condensate
• Dynamics in spinor condensates controlled by a microwave dressing field
• Reinforcement Learning for Many-Body Ground-State Preparation Inspired by Counterdiabatic Driving
• Measurement Based Feedback Quantum Control With Deep Reinforcement Learning for Double-well Non-linear Potential
• Deep Reinforcement Learning for Quantum State Preparation with Weak Nonlinear Measurements
• Reinforcement Learning assisted Quantum Optimization
• Deep Reinforcement Learning Control of Quantum Cartpoles
• Dynamic stabilization of a quantum many-body spin system
• Nonlinear interferometry beyond classical limit facilitated by cyclic dynamics
• Long-lived non-thermal states realized by atom losses in one-dimensional quasi-condensates
• Improving the dynamics of quantum sensors with reinforcement learning
• Characterizing the energy gap and demonstrating an adiabatic quench in an interacting spin system
• Variational quantum compiling with double Q-learning
• Faster State Preparation across Quantum Phase Transition Assisted by Reinforcement Learning
• Quantum Adiabatic Algorithm Design using Reinforcement Learning
• Classifying global state preparation via deep reinforcement learning
• Quantum noise in three-dimensional BEC interferometry
• Efficient and Robust Entanglement Generation with Deep Reinforcement Learning for Quantum Metrology

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