Reinforcement Learning Enhancing Entanglement for Two-Photon-Driven Rabi Model

A control scheme is proposed that leverages reinforcement learning to enhance entanglement by modulating the two-photon-driven amplitude in a Rabi model. The quantum phase diagram versus the amplitude of the two-photon process and the coupling between the cavity field and the atom in the Rabi model, is indicated by the energy spectrum of the hybrid system, the witness of entanglement, second order correlation, and negativity of Wigner function. From a dynamical perspective, the behavior of entanglement can reflect the phase transition and the reinforcement learning agent is employed to produce temporal sequences of control pulses to enhance the entanglement in the presence of dissipation. The entanglement can be enhanced in different parameter regimes and the control scheme exhibits robustness against dissipation. The replaceability of the controlled system and the reinforcement learning module demonstrates the generalization of this scheme. This research paves the way of positively enhancing quantum resources in non-equilibrium systems.A control scheme is proposed that leverages reinforcement learning to enhance entanglement by modulating the two-photon-driven amplitude in a Rabi model. The quantum phase diagram versus the amplitude of the two-photon process and the coupling between the cavity field and the atom in the Rabi model, is indicated by the energy spectrum of the hybrid system, the witness of entanglement, second order correlation, and negativity of Wigner function. From a dynamical perspective, the behavior of entanglement can reflect the phase transition and the reinforcement learning agent is employed to produce temporal sequences of control pulses to enhance the entanglement in the presence of dissipation. The entanglement can be enhanced in different parameter regimes and the control scheme exhibits robustness against dissipation. The replaceability of the controlled system and the reinforcement learning module demonstrates the generalization of this scheme. This research paves the way of positively enhancing quantum resources in non-equilibrium systems.

arXiv:2411.15841Nov 24, 20240 citations

Reinforcement Learning Methods for Quantum Control

Michèle Desouter-Lecomte

May 23, 20230 citations

Enhancing light-matter coupling for exploring chaos in the quantum Rabi model

Yan-Song Hu, Yuan Qiu, Ye-Hong Chen +2 more

Accessing chaos in the quantum Rabi model (QRM) usually requires operating far from resonance, combined with ultra- or deep-strong light-matter coupling. This makes direct experiments challenging. In this manuscript, we propose a solution to this challenge by employing an anti-squeezing transformation to the bosonic field. Specifically, we demonstrate that this transformation maps a weakly coupled, two-photon driven Jaynes-Cummings model (JCM) to an effective deep-strong-coupling QRM in the squeezed-light frame. Using out-of-time-order correlator, Husimi distribution, and linear entanglement entropy, we numerically probe chaos in this coupling-enhanced platform and observe the similar chaotic phenomena as in the ideal QRM. We also find the coupling-enhanced model can drive the system deeper into the chaotic regime. This establishes coupling-enhanced method as a practical approach to study QRM chaos without requiring intrinsic ultra-strong coupling.Accessing chaos in the quantum Rabi model (QRM) usually requires operating far from resonance, combined with ultra- or deep-strong light-matter coupling. This makes direct experiments challenging. In this manuscript, we propose a solution to this challenge by employing an anti-squeezing transformation to the bosonic field. Specifically, we demonstrate that this transformation maps a weakly coupled, two-photon driven Jaynes-Cummings model (JCM) to an effective deep-strong-coupling QRM in the squeezed-light frame. Using out-of-time-order correlator, Husimi distribution, and linear entanglement entropy, we numerically probe chaos in this coupling-enhanced platform and observe the similar chaotic phenomena as in the ideal QRM. We also find the coupling-enhanced model can drive the system deeper into the chaotic regime. This establishes coupling-enhanced method as a practical approach to study QRM chaos without requiring intrinsic ultra-strong coupling.

quant-pharXiv:2603.09114New Journal of PhysicsMar 11, 20260 citations

Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems

Jagjit Singh Dhatterwal, Sandeep Kumar, Shilpa Choudhary

The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate $\mathbf{2 0}-\mathbf{3 5 \%}$ faster convergence and $\mathbf{1 5 \%}$ higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate $\mathbf{2 0}-\mathbf{3 5 \%}$ faster convergence and $\mathbf{1 5 \%}$ higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.

International Conference on Mobile Networks and Wireless Communications (ICMNWC)Dec 10, 20250 citations

\mathbf{2 0}-\mathbf{3 5 \%}

faster convergence and

\mathbf{1 5 \%}

higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.