Emerging Cooperation through Quantum Entanglement in Multi-Agent Systems

Emerging Cooperation through Quantum Entanglement in Multi-Agent Systems

Abstract:

This thesis investigates the feasibility of quantum entanglement enhancing cooperation in multi-agent reinforcement learning. Utilizing the iterated prisoner dilemma as a benchmark, we propose a decentralized multi-agent reinforcement learning framework where two MARL agents equipped with variational quantum circuits can affect each other through quantum entanglement. Unlike existing approaches that rely on dedicated quantum communication channels, this thesis examines whether entanglement alone can facilitate cooperative equilibria. Therefore, we evaluate the effects of different entanglement architectures to develop mutual cooperative strategies that escape the Nash equilibrium. The results of our experiments indicate that while entanglement can facilitate strategies that outperform the defection baseline, long-term cooperative behavior remains unfeasible, suggesting that quantum correlations alone are insufficient to sustain cooperative strategies in multi-agent reinforcement learning settings.

Author:

Marvin Heinrich

Advisors:

Michael Kölle, Leo Sünkel, Claudia Linnhoff-Popien

Student Thesis | Published March 2025 | Copyright © QAR-Lab
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