Regensburg 2025 – wissenschaftliches Programm

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TT: Fachverband Tiefe Temperaturen

TT 28: Many-body Quantum Dynamics II (joint session DY/TT)

TT 28.13: Vortrag

Mittwoch, 19. März 2025, 12:45–13:00, H37

Entanglement Transitions in Quantum Games through Reinforcement Learning — •Giovanni Cemin¹, Marin Bukov¹, and Markus Schmitt^2,3 — ¹Max Planck Institute for the Physics of Complex Systems, Dresden, Germany — ²University of Regensburg, Regensburg, Germany — ³Forschungszentrum Jülich, Institute of Quantum Control, Jülich, Germany

In this research, we investigate the dynamics of entanglement in Cliord circuits by employing a reinforcement learning (RL) algorithm in competition with a random agent. The RL agent is designed to strategically place gates that decrease entanglement, while the random agent aims to increase entanglement. This interaction between the two agents results in an entanglement transition, the nature of which is induced by the level of information accessible by the RL agent. By systematically varying the information provided to the RL agent, we analyze its impact on the transition characteristics. Our findings provide new insights into the interplay between entanglement manipulation and information constraints, shedding light on the fundamental mechanisms governing quantum circuit dynamics.

Keywords: Reinforcement learning; quantum circuits; clifford circuits; entanglement; phase transitions