Learning to stabilize nonequilibrium phases of matter with active feedback using partial information
Year: 2026
Type: article
Abstract: Recent advances in quantum computing experiments have enabled new pathways for feedback control. Here, we investigate the role of information in interactive quantum many-body dynamics to engineer out-of-equilibrium phases of matter. To this end, we train reinforcement learning agents to prevent entanglement spreading in $(1{+}1)$-dimensional stabilizer circuits with up to $128$ qubits. We find that, above an information threshold, agents learn stochastic near-optimal strategies that prevent the system from reaching volume-law entangled states and instead stabilize area-law scaling. These strategies rely on bottlenecks that divide the system and cap the entanglement in the steady state. Crucially, learned strategies are inherently out of equilibrium, require real-time active feedback, and could not be reproduced by simple human-designed control rules. This work establishes the foundations for 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.
Authors: Giovanni Cemin, Markus Schmitt, Marin Bukov
Language: English
Topic: Quantum many-body systems
Subfield: Atomic and Molecular Physics, and Optics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,000
OpenAlex ID: W4416176231
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