Critical States Preparation With Deep Reinforcement Learning
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arXiv
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| Format: | Preprint |
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2026
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| author | Yu, Jia-Wen Yu, Yi-Ming Yan, Ke-Xiong Lin, Jun-Hao Song, Jie Chen, Ye-Hong Xia, Yan |
| author_facet | Yu, Jia-Wen Yu, Yi-Ming Yan, Ke-Xiong Lin, Jun-Hao Song, Jie Chen, Ye-Hong Xia, Yan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_09135 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Critical States Preparation With Deep Reinforcement Learning Yu, Jia-Wen Yu, Yi-Ming Yan, Ke-Xiong Lin, Jun-Hao Song, Jie Chen, Ye-Hong Xia, Yan Quantum Physics 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. |
| title | Critical States Preparation With Deep Reinforcement Learning |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2603.09135 |