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An energy gap develops near quantum critical point of quantum phasetransition in a finite many-body (MB) system, facilitating the ground statetransformation by adiabatic parameter change. In real application scenarios,however, the efficacy for such a protocol is compromised by the need to balancefinite system life time with adiabaticity, as exemplified in a recentexperiment that prepares three-mode balanced Dicke state near deterministically[PNAS {\bf 115}, 6381 (2018)]. Instead of tracking the instantaneous groundstate as unanimously required for most adiabatic crossing, this work reports afaster sweeping policy taking advantage of excited level dynamics. It isobtained based on deep reinforcement learning (DRL) from a multi-step trainingscheme we develop. In the absence of loss, a fidelity $\ge 99\%$ betweenprepared and the target Dicke state is achieved over a small fraction of theadiabatically required time. When loss is included, training is carried outaccording to an operational benchmark, the interferometric sensitivity of theprepared state instead of fidelity, leading to better sensitivity in about halfof the previously reported time. Implemented in a Bose-Einstein condensate of$\sim 10^4$ $^{87}$Rb atoms, the balanced three-mode Dicke state exhibiting animproved number squeezing of $13.02\pm0.20$ dB is observed within 766 ms,highlighting the potential of DRL for quantum dynamics control and quantumstate preparation in interacting MB systems.

Faster State Preparation across Quantum Phase Transition Assisted by Reinforcement Learning