内在可解释性在量子机器学习中具有内在价值
Inherent interpretability provides inherent value in quantum machine learning
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中文总结 AI 辅助
研究探讨量子机器学习模型价值，认为可通过内在可解释性表征。以量子傅里叶模型等为例，展示其与随机傅里叶特征在GP核设计等方面的不同，回顾相关示例，强调应分开评估量子模型内在组件与任务性能，因其内在可解释性或推动实际应用。
中文摘要 AI 辅助
量子机器学习（QML）领域发展到重视那些被认为最能直接与经典机器学习中实用模型相媲美的模型，即大规模神经网络。近来，经典机器学习在实际应用不可解释神经网络方面得到了惨痛教训：模型可解释性对特定领域协同设计和人类应用很重要。我们从更广泛的机器学习视角认为，量子机器学习模型的价值可通过其内在可解释性来表征，即其数学结构对特定机器学习任务中期望的模型行为有重要贡献。为支持这一观点，我们给出一个以量子傅里叶模型和随机傅里叶特征作为近似高斯过程（GP）核的方法，用于机器学习中不确定性量化任务的表征示例。两种数学结构的自上而下和自下而上的互补性表明，量子傅里叶模型与随机傅里叶特征在有原则的GP核设计以及利用真实世界数据进行不确定性量化的可解释发现方面提供了不同工具。为展示量子信息工具带来的丰富归纳偏差，我们回顾了量子机器学习文献中的示例——包括对称性、度量几何和拓扑——可用于为特定任务设计内在可解释的机器学习模型。我们希望这种框架能鼓励量子机器学习社区将量子模型的内在组件和机制与任务性能分开评估，因为内在可解释性可能是量子模型乃至量子计算机在实际机器学习中被使用的原因。
英文摘要
The field of quantum machine learning (QML) evolved to value models believed to most directly rival those providing utility in classical ML, namely large-scale neural networks. Although more recently, classical ML has been learning a hard lesson with respect to deploying un-interpretable neural networks in the wild: model interpretability matters for domain-adapted co-design and human adoption. We adopt this larger ML perspective to argue that quantum ML model value can be found through the characterization of its inherent interpretability offerings -- i.e. its mathematical structure that contributes meaningfully to desired model behavior for the specific ML task. To support our perspective, we provide a motivating example of a characterization with quantum Fourier models and random Fourier features (RFF) as approaches to approximate Gaussian process (GP) kernels for uncertainty quantification tasks in ML. The top-down and bottom-up complementarity of the two mathematical constructions reveals that quantum Fourier models offer different tools than RFFs for principled GP kernel design and interpretable discovery for uncertainty quantification with real-world data. To showcase the rich variety of inductive biases enabled by quantum information tools, we review examples from the QML literature -- including symmetry, metric geometry, and topology -- that can be used to design inherently interpretable ML models for specific tasks. We hope this framing encourages the QML community to value the inherent components and mechanisms of quantum models separately from task performance, as inherent interpretability might be the reason that a quantum model, and potentially a quantum computer, gets used in practice for ML.
