Critical States Preparation With Deep Reinforcement Learning

The fast and efficient preparation of quantum critical states is a challenging yet crucial task for various quantum technologies. This difficulty is most particularly for systems near a quantum phase transition, where the closure of the energy gap fundamentally limits the timescale of adiabatic processes and thus precludes rapid state preparation. We propose a framework using deep reinforcement learning (DRL) to rapidly prepare quantum critical states, with broad extendibility to light-matter interaction systems. Specifically, a DRL agent optimizes a set of time-dependent control Hamiltonians to drive the system from an initial noncritical state to a target critical state within a finite time and over experimentally accessible parameter ranges. As a concrete application, we focus on the quantum Rabi model. The DRL-optimized time-dependent control Hamiltonian yield a final state with high-fidelity ($>0.999$) to the target critical state. The protocol can be readily extended to other quantum critical systems described by light-matter interaction models, such as quantum Dicke model. This investigation provides a powerful new framework for preparing and manipulating quantum critical states.

💡 Research Summary

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The paper introduces a novel framework that leverages deep reinforcement learning (DRL) to prepare quantum critical states rapidly and with high fidelity. Quantum critical points are characterized by a vanishing energy gap, making adiabatic state preparation impractically slow. Traditional gradient‑based optimal control methods require explicit knowledge of the system dynamics and often fail in strongly or ultrastrongly coupled regimes where analytical gradients are unreliable. To overcome these limitations, the authors formulate the preparation task as a model‑free optimization problem and employ a DRL agent to discover time‑dependent control Hamiltonians that drive a system from a non‑critical initial ground state to a target critical ground state within a finite, experimentally feasible time.

The general setting is a Hamiltonian (H_{\text{tot}}(t)=H