Quantum Advantage in Multi Agent Reinforcement Learning
Simranjeet Singh Dahia, Claudia Szabo

TL;DR
This paper empirically demonstrates quantum advantage in multi-agent reinforcement learning through entangled quantum agents, showing improved performance over classical and unentangled quantum approaches.
Contribution
It provides the first rigorous evidence of quantum advantage in multi-agent RL using entangled quantum circuits and explores the impact of entanglement structures.
Findings
Entangled QMARL agents surpass the classical CHSH game performance ceiling.
Unentangled quantum circuits match classical performance, highlighting entanglement as the key factor.
Hybrid quantum-classical agents outperform fully classical and fully quantum solutions in cooperative navigation.
Abstract
We present an empirical evaluation of quantum entanglement in agent coordination within quantum multi agent reinforcement learning (QMARL). While QMARL has attracted growing interest recently, most prior work evaluates quantum policies without provable baselines, making it impossible to rigorously distinguish quantum advantage from algorithmic coincidence. We address this directly by evaluating a decentralized QMARL framework with variational quantum circuit (VQC) actors with shared entangled states. In the CHSH game, which has a mathematically proven classical performance ceiling of 0.75 win rate, we show that entangled QMARL agents approach the Tsirelson limit of 0.854, providing clear evidence of their quantum advantage. We show that unentangled quantum circuits match the classical baseline, confirming that entanglement and not the quantum circuit itself is the active coordination…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
