Learning to stabilize nonequilibrium phases of matter with active feedback using partial information

TL;DR

This paper demonstrates how reinforcement learning with partial information can actively control quantum many-body systems, stabilizing nonequilibrium phases and reducing entanglement through non-greedy, stochastic strategies that induce system bottlenecks.

Contribution

It introduces reinforcement learning strategies for active feedback control in quantum systems, enabling stabilization of nonequilibrium phases with partial information, a novel approach in quantum control.

Findings

RL agents reduce entanglement from volume-law to area-law scaling

Strategies involve placing bottlenecks to split the system and control entanglement

Learned behaviors are inherently out of equilibrium and cannot be mimicked by simple rules

Abstract

We investigate the role of information in active feedback control of quantum many-body systems using reinforcement learning. Active feedback breaks detailed balance, enabling the engineering of steady states and dynamical phases of matter otherwise inaccessible in equilibrium. We train reinforcement learning agents using partial state information to prevent entanglement spreading in (1+1)-dimensional stabilizer circuits with up to 128 qubits. We find that, above a critical information threshold, learned near-optimal strategies are non-greedy, stochastic, and reduce volume-law entangled steady states to area-law scaling. The agents achieve this by placing a series of bottlenecks that induce pyramidal structures in the long-time spatial entanglement distribution, which effectively split the system and reduce the maximum accessible entanglement. Crucially, learned strategies are inherently out of equilibrium and require real-time active feedback; we find that the learned behavior cannot be replaced by simple human-designed control rules. This work establishes the foundations for classically implemented, information-driven individual control of many interacting quantum degrees of freedom, demonstrating the capabilities of reinforcement learning to stabilize and uncover novel critical properties of many-body nonequilibrium steady states.