论文标题
可解释量子机学习的AI
eXplainable AI for Quantum Machine Learning
论文作者
论文摘要
参数化的量子电路（PQC）为机器学习提供了新的方法（ML）。但是，从计算的角度来看，它们对现有的可解释AI（XAI）方法提出了挑战。一方面，量子电路的测量引入了概率误差，这会影响这些方法的收敛性。另一方面，量子电路的相空间随量子数的数量而呈指数式扩展，这使在多项式时间内执行XAI方法的努力变得复杂。在本文中，我们将讨论已建立的XAI方法的性能，例如基线形状和集成梯度。使用PQC的内部力学，我们研究了加快其计算的方法。
Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML). However, from a computational point of view they present a challenge to existing eXplainable AI (xAI) methods. On the one hand, measurements on quantum circuits introduce probabilistic errors which impact the convergence of these methods. On the other hand, the phase space of a quantum circuit expands exponentially with the number of qubits, complicating efforts to execute xAI methods in polynomial time. In this paper we will discuss the performance of established xAI methods, such as Baseline SHAP and Integrated Gradients. Using the internal mechanics of PQCs we study ways to speed up their computation.
