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A Quantum Algorithm for Shapley Value Estimation
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In the classical context, the cooperative game theory concept of the Shapley value has been adapted for post hoc explanations of machine learning models. However, this approach does not easily translate to eXplainable Quantum ML (XQML). Finding Shapley values can be highly computationally complex. We propose quantum algorithms which can extract Shapley values within some confidence interval. Our results perform in polynomial time. We demonstrate the validity of each approach under specific examples of cooperative voting games.
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Cited by 2 Pith papers
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QuXAI: Explainers for Hybrid Quantum Machine Learning Models
Q-MEDLEY estimates global feature importance in hybrid quantum-classical models by averaging drop-column and permutation importance, re-evaluating the quantum feature map after each perturbation.
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A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI
Quantum amplitude estimation can estimate Shapley values with O(1/ε) queries to the value function, a quadratic improvement over classical Monte Carlo's O(σ²/ε²).
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