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)和特别是机器学习(ML)方法日益复杂,同时对人们的生活产生越来越多的影响。这导致可以解释的AI(XAI)作为一个重要的研究领域,有助于人类更好地理解 ML 系统。与此同时,量子机器学习(QML)随着量子计算硬件不断改进,加上通过云服务增加可用性而出现。QML使得量子增强ML能够解释这种电路,利用量子力来帮助ML任务,典型的形式是将量子和经典资源相结合的量子级混合算法。量子门门门构成基于门的量子硬件和可被用于量子计算的重要研究领域。对于QML应用而言,量子机器学习(QML)正在出现,随着量子计算硬件硬件硬件的不断改进,其参数正在以传统方式优化。在XAII的启发下,我们提出了这种电路路路路路的可解释问题,通过量化(集团)光门对具体目标的重要性。为此,我们转移并调整了基于门的可变性结构的可解释性,从而推算出精确地解释成本。</s>