Model-based RL trains a neural network embedding the Hamiltonian to output robust pulses for arbitrary rotations under varying parameters in a single-spin system, achieving GRAPE-comparable fidelity while revealing consistent phase structures.
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VF-QCTRL combines LLMs with physics-informed symbolic reasoning and optimization to produce analytic control protocols that match or exceed conventional solvers across a new 16-task benchmark spanning single/multi-qubit, closed/open, and noisy systems.
A comprehensive review organizing progress at the AI-quantum information intersection from both directions.
citing papers explorer
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Uncovering Latent Structures in Robust Pulse Sequences: A Model-Based Reinforcement Learning Approach for Adaptable Quantum Control
Model-based RL trains a neural network embedding the Hamiltonian to output robust pulses for arbitrary rotations under varying parameters in a single-spin system, achieving GRAPE-comparable fidelity while revealing consistent phase structures.
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Toward General Quantum Control with Physics-Informed Large Language Models
VF-QCTRL combines LLMs with physics-informed symbolic reasoning and optimization to produce analytic control protocols that match or exceed conventional solvers across a new 16-task benchmark spanning single/multi-qubit, closed/open, and noisy systems.
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When AI meets quantum information: A comprehensive review
A comprehensive review organizing progress at the AI-quantum information intersection from both directions.