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.
翻译:量子电路(PQCs)为机器学习提供了一种新颖的方法。 但是,从计算的角度来看,它们对现有的可氧化的AI(xAI)方法提出了挑战。一方面,量子电路的测量引入了概率错误,影响了这些方法的趋同。另一方面,量子电路的阶段空间随着qubit数量而成倍扩大,使在多球时间执行 xAI 方法的努力复杂化。在本文中,我们将讨论既有的 xAI 方法的性能,例如基线 SHAP 和集成梯度。我们利用PQC 的内部机械学来研究加速计算的方法。