AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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This paper considers an extended Gutoski-Watrous game in which each player's quantum strategy is represented by a local density matrix, and derives tensor-contraction expressions for the payoff functions and their gradients, thereby avoiding the explicit construction of the full joint density matrix.
The Unitary Strategy Matrix Exponential Algorithm (USMEA) is proposed, a geometry-aware sequential algorithm for the EEWL mixed-strategy setting, in which each player jointly learns a trainable set of local unitary actions and the associated classical mixing probabilities.
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