- Title
- Control Hamiltonian selection for quantum state stabilization using deep reinforcement learning
- Creator
- Song, Chunxiang; Liu, Yanan; Dong, Daoyi; Mo, Huadong
- Relation
- 2024 43rd Chinese Control Conference (CCC). Proceedings of the 43rd Chinese Control Conference (Kunming 28-31 July, 2024) p. 6783-6788
- Publisher Link
- http://dx.doi.org/10.23919/ccc63176.2024.10662695
- Publisher
- IEEE
- Resource Type
- conference paper
- Date
- 2024
- Description
- Quantum state stabilization is a pivotal element in the realm of quantum control, forming the bedrock for various quantum tasks. To achieve the stabilization of a quantum state, it is imperative to formulate effective control channels (represented by control Hamiltonians) and devise the appropriate control signals. In this study, we introduce a novel approach, the selection of control Hamiltonians through Deep reinforcement learning (SCH-DRL), to address the challenge of control Hamiltonian selection in quantum control. Deep reinforcement learning (DRL) is employed to generate control signals corresponding to control Hamiltonians, and SCH-DRL utilizes these control signals to recognize a set of simple and efficient control Hamiltonians, depending on different target states. This approach not only provides a method for control Hamiltonian selection in quantum state stabilization but also unveils the untapped potential of DRL for a broad spectrum of applications in the field of quantum information. Through applications in two-qubit and three-qubit scenarios, we demonstrate how the SCH-DRL method adeptly selects the quantity of control Hamiltonians for achieving the desired stability of quantum states.
- Subject
- quantum state stabilization; control Hamiltonian selection; deep reinforcement learning (DRL); quantum control
- Identifier
- http://hdl.handle.net/1959.13/1520429
- Identifier
- uon:57476
- Identifier
- ISBN:9789887581581
- Identifier
- ISSN:1934-1768
- Language
- eng
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