Reinforcement learning under partial observability with a stochastic terminal reward from one-shot Hamiltonian measurements enables experiment-compatible preparation of Bose-Hubbard ground states and GHZ states.
Manipulation of Spin Dynamics by Deep Reinforcement Learning Agent
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abstract
We implement the reinforcement learning agent for a spin-1 atomic system to prepare spin squeezed state from given initial state. Proximal policy gradient (PPO) algorithm is used to deal with continuous external control field and final optimized protocol is given by a stochastic policy. In both mean-field system and two-body quantum system, RL agent finds the optimal policies. In many-body quantum system, it also gives polices that outperform purely greedy policy and optimized adiabatic passage. These polices given by RL agent have good physical interpretability in phase space and may help us to understand quantum dynamics. In fact, RL could be highly versatile in quantum optimal control problems.
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quant-ph 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
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Experiment-compatible measurement--feedback quantum state preparation with reinforcement learning
Reinforcement learning under partial observability with a stochastic terminal reward from one-shot Hamiltonian measurements enables experiment-compatible preparation of Bose-Hubbard ground states and GHZ states.