剖析量子机器学习的可解释性潜力与局限，提出首个专用解释方法。
Opportunities and limitations of explaining quantum machine learning
- 提出两种专为量子机器学习设计的新型解释方法。
- 系统梳理该领域可解释性研究的前景与挑战。
- 适合关注量子模型可信度与未来发展的研究者。
许多机器学习模型的输出难以理解和解释。尽管神经网络的可解释性研究近年活跃，但量子机器学习模型的可解释性仍知之甚少。本文首次系统梳理该领域的研究前景，提出两种针对量子学习模型的全新解释方法。通过比较现有与新方法，揭示量子机器学习在可解释性方面的机遇与限制。研究有助于推动该领域可持续发展，避免未来信任危机。
原文摘要 · Abstract (English)
A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. Despite a few recent works analyzing some specific aspects of explainability, as of now there is no clear big picture perspective as to what can be expected from quantum learning models in terms of explainability. In this work, we address this issue by identifying promising research avenues in this direction and lining out the expected future results. We additionally propose two explanation methods designed specifically for quantum machine learning models, as first of their kind to the best of our knowledge. Next to our pre-view of the field, we compare both existing and novel methods to explain the predictions of quantum learning models. By studying explainability in quantum machine learning, we can contribute to the sustainable development of the field, preventing trust issues in the future.
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