Bayesian agents in iterated quantum games learn shared entanglement levels and achieve quantum advantage or dominant strategies when mutual beliefs align, with strong entanglement beliefs acting as a trust proxy even without actual entanglement.
Quantum strategies
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abstract
We consider game theory from the perspective of quantum algorithms. Strategies in classical game theory are either pure (deterministic) or mixed (probabilistic). We introduce these basic ideas in the context of a simple example, closely related to the traditional Matching Pennies game. While not every two-person zero-sum finite game has an equilibrium in the set of pure strategies, von Neumann showed that there is always an equilibrium at which each player follows a mixed strategy. A mixed strategy deviating from the equilibrium strategy cannot increase a player's expected payoff. We show, however, that in our example a player who implements a quantum strategy can increase his expected payoff, and explain the relation to efficient quantum algorithms. We prove that in general a quantum strategy is always at least as good as a classical one, and furthermore that when both players use quantum strategies there need not be any equilibrium, but if both are allowed mixed quantum strategies there must be.
fields
quant-ph 1years
2024 1verdicts
UNVERDICTED 1representative citing papers
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Bayesian rational agents in iterated quantum games
Bayesian agents in iterated quantum games learn shared entanglement levels and achieve quantum advantage or dominant strategies when mutual beliefs align, with strong entanglement beliefs acting as a trust proxy even without actual entanglement.