Critical States Preparation With Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2603.09135v1
• Date: Tue, 10 Mar 2026 03:17:42 GMT
• Title: Critical States Preparation With Deep Reinforcement Learning
• Abstract summary: We propose a framework using deep reinforcement learning (DRL) to rapidly prepare quantum critical states.<n> Specifically, a DRL agent optimize a set of time-dependent control Hamiltonians to drive the system from an initial noncritical state to a target critical state.<n>The protocol can be readily extended to other quantum critical systems described by light-matter interaction models.
• Score: 10.93309469378866
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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