aiqbench analysis
This research proposes using neutral atom arrays to solve NP-Hard Nash equilibrium problems by mapping game theory constraints onto Rydberg ground states. The approach leverages quantum simulation capabilities to tackle optimization challenges traditionally difficult for classical computers, specifically within the PPAD complexity class. By bridging game theory with quantum hardware, this work demonstrates a concrete pathway for quantum advantage in complex system modeling.
Mapping Game Theory to Quantum Systems: Nash Equilibria via Neutral Atom Computing
arXiv:2511.09841v2 Announce Type: replace Abstract: Nash equilibria are crucial for understanding game behavior and systems in economics, physics, biology, and computer science. A significant application arises from the connection between Nash equilibria and optimization problems . However, finding Nash equilibria is challenging due to its NP-Hard complexity, specifically within the PPAD class. By exploiting the correspondence between Maximum Independent Sets (MIS) and Nash equilibria on unit-disk graphs, we map these problems onto the ground state configurations of Rydberg atom arrays. Simulations show the effectiveness of this quantum method, highlighting its potential for solving complex problems in game theory.
Abstract:Nash equilibria are crucial for understanding game behavior and systems in economics, physics, biology, and computer science. A significant application arises from the connection between Nash equilibria and optimization problems . However, finding Nash equilibria is challenging due to its NP-Hard complexity, specifically within the PPAD class. By exploiting the correspondence between Maximum Independent Sets (MIS) and Nash equilibria on unit-disk graphs, we map these problems onto the ground state configurations of Rydberg atom arrays. Simulations show the effectiveness of this quantum method, highlighting its potential for solving complex problems in game theory.
arXiv:2511.09841 [quant-ph]
(or arXiv:2511.09841v2 [quant-ph] for this version)
https://doi.org/10.48550/arXiv.2511.09841 Focus to learn more arXiv-issued DOI via DataCite
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