用量子算法加速解释量子AI决策，速度比经典方法快得多
A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI
- 用量子算法估算博弈论中的谢泼利值，实现高效可解释性
- 在多种场景下比经典蒙特卡洛方法快至少平方级，误差可控
- 适合研究量子AI可解释性或想提速解释计算的研究者
本文致力于开发量子人工智能算法的高效后验解释方法。在经典场景中，合作博弈论中的谢泼利值可自然用于后验解释，识别影响AI决策的关键因素。一个关键问题是：如何将谢泼利值推广到量子设置，并利用量子效应加速其计算？我们提出量子算法，可在一定置信区间内提取谢泼利值。该方法在多种情况下可实现对经典蒙特卡洛近似方法的二次加速，且仅受多对数因子影响。我们通过具体投票博弈实证验证了方法有效性，并为一般合作博弈提供了严格的性能证明。
原文摘要 · Abstract (English)
This work focuses on developing efficient post-hoc explanations for quantum AI algorithms. In classical contexts, the cooperative game theory concept of the Shapley value adapts naturally to post-hoc explanations, where it can be used to identify which factors are important in an AI's decision-making process. An interesting question is how to translate Shapley values to the quantum setting and whether quantum effects could be used to accelerate their calculation. We propose quantum algorithms that can extract Shapley values within some confidence interval. Our method is capable of quadratically outperforming classical Monte Carlo approaches to approximating Shapley values up to polylogarithmic factors in various circumstances. We demonstrate the validity of our approach empirically with specific voting games and provide rigorous proofs of performance for general cooperative games.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。