A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917987358867456 |
|---|---|
| author | Burge, Iain Barbeau, Michel Garcia-Alfaro, Joaquin |
| author_facet | Burge, Iain Barbeau, Michel Garcia-Alfaro, Joaquin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14639 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI Burge, Iain Barbeau, Michel Garcia-Alfaro, Joaquin Quantum Physics Artificial Intelligence Cryptography and Security 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. |
| title | A Shapley Value Estimation Speedup for Efficient Explainable Quantum AI |
| topic | Quantum Physics Artificial Intelligence Cryptography and Security |
| url | https://arxiv.org/abs/2412.14639 |