eXplainable AI for Quantum Machine Learning
This work addresses the problem of making quantum machine learning more interpretable for researchers and practitioners, but it appears incremental as it builds on existing xAI methods.
The paper tackles the challenge of applying explainable AI (xAI) methods to Parametrized Quantum Circuits (PQCs) in quantum machine learning, focusing on issues like probabilistic errors and exponential phase space complexity, and explores ways to speed up computations using PQC mechanics.
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.