Methods of artificial intelligence (AI) and especially machine learning (ML) have been growing ever more complex, and at the same time have more and more impact on people's lives. This leads to explainable AI (XAI) manifesting itself as an important research field that helps humans to better comprehend ML systems. In parallel, quantum machine learning (QML) is emerging with the ongoing improvement of quantum computing hardware combined with its increasing availability via cloud services. QML enables quantum-enhanced ML in which quantum mechanics is exploited to facilitate ML tasks, typically in form of quantum-classical hybrid algorithms that combine quantum and classical resources. Quantum gates constitute the building blocks of gate-based quantum hardware and form circuits that can be used for quantum computations. For QML applications, quantum circuits are typically parameterized and their parameters are optimized classically such that a suitably defined objective function is minimized. Inspired by XAI, we raise the question of explainability of such circuits by quantifying the importance of (groups of) gates for specific goals. To this end, we transfer and adapt the well-established concept of Shapley values to the quantum realm. The resulting attributions can be interpreted as explanations for why a specific circuit works well for a given task, improving the understanding of how to construct parameterized (or variational) quantum circuits, and fostering their human interpretability in general. An experimental evaluation on simulators and two superconducting quantum hardware devices demonstrates the benefits of the proposed framework for classification, generative modeling, transpilation, and optimization. Furthermore, our results shed some light on the role of specific gates in popular QML approaches.

翻译：人工智能（AI）方法，尤其是机器学习（ML）方法日益复杂，同时其对人们生活的影响也愈发显著。这使得可解释人工智能（XAI）成为一个重要的研究领域，旨在帮助人类更好地理解机器学习系统。与此同时，随着量子计算硬件的持续改进及其通过云服务的可用性不断提高，量子机器学习（QML）正在兴起。量子机器学习实现了量子增强的机器学习，利用量子力学来辅助完成机器学习任务，通常表现为结合量子与经典资源的量子-经典混合算法。量子门构成了基于门的量子硬件的基本构建模块，并形成可用于量子计算的电路。对于量子机器学习应用，量子电路通常是参数化的，其参数通过经典方式优化，使得适当定义的目标函数最小化。受可解释人工智能启发，我们通过量化（组）门对特定目标的重要性来探讨此类电路的可解释性问题。为此，我们将成熟的沙普利值概念迁移并适配到量子领域。得到的归因结果可解释为：为何特定电路在给定任务中表现出色，从而增进对如何构建参数化（或变分）量子电路的理解，并总体上提升其人类可解释性。在模拟器和两台超导量子硬件设备上的实验评估表明，所提出的框架在分类、生成建模、编译和优化方面均具有优势。此外，我们的研究结果揭示了特定门在主流量子机器学习方法中的作用。