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原文摘要

Imperfect-information games with a graded-reward structure present significant computational challenges for equilibrium computation, since the payoff expectations must be estimated across exponentially large strategy spaces. Counterfactual Regret Minimization over the years has proven effective for approximating Nash equilibria, but Monte Carlo variants create a large overhead of sampling costs when the counterfactual values are estimated from sampled game-tree trajectories. We propose a novel Quantum-Assisted Counterfactual Regret Minimization, a framework that uses Quantum Amplitude Estimation to reduce the computational complexity of the regret updates in bounded-payoff imperfect-information games, while preserving the convergence of its classical counterpart. We give a theoretical analysis of the resulting exploitability bound, discuss NISQ-compatible amplitude-estimation variants, the structure of their resource requirements, and outline what is needed to validate the potential advantage empirically.