Reinforcement learning for autonomous preparation of Floquet-engineered states: Inverting the quantum Kapitza oscillator

TL;DR

This paper demonstrates how reinforcement learning can autonomously prepare and control Floquet-engineered quantum states, specifically stabilizing the inverted position of a quantum Kapitza oscillator under strong periodic driving, even with noisy data.

Contribution

It introduces a model-free RL approach to control out-of-equilibrium quantum systems, leveraging intra-period dynamics often overlooked in Floquet engineering, without prior physical system knowledge.

Findings

RL successfully stabilizes the inverted state of the quantum Kapitza oscillator.

The method is robust to noise and random failures in control sequences.

Intra-period dynamics can outperform stroboscopic control at moderate drive frequencies.

Abstract

I demonstrate the potential of reinforcement learning (RL) to prepare quantum states of strongly periodically driven non-linear single-particle models. The ability of Q-Learning to control systems far away from equilibrium is exhibited by steering the quantum Kapitza oscillator to the Floquet-engineered stable inverted position in the presence of a strong periodic drive within several shaking cycles. The study reveals the potential of the intra-period (micromotion) dynamics, often neglected in Floquet engineering, to take advantage over pure stroboscopic control at moderate drive frequencies. Without any knowledge about the underlying physical system, the algorithm is capable of learning solely from tried protocols and directly from simulated noisy quantum measurement data, and is stable to noise in the initial state, and sources of random failure events in the control sequence. Model-free RL can provide new insights into automating experimental setups for out-of-equilibrium systems undergoing complex dynamics, with potential applications in quantum information, quantum optics, ultracold atoms, trapped ions, and condensed matter.