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Trapped Ion Quantum Computing Quantum Machine Learning

eXplainable AI for Quantum Machine Learning

arXiv
Authors: Patrick Steinmüller, Tobias Schulz, Ferdinand Graf, Daniel Herr

Year

2022

Paper ID

57735

Status

Preprint

Abstract Read

~2 min

Abstract Words

107

Citations

N/A

Abstract

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.

Why This Paper Matters

  • This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
  • It adds a 2022 reference point for readers tracking recent quantum research.
  • Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML).

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References & Citation Signals

[1] DOI https://doi.org/arXiv:2211.01441 [2] arXiv https://arxiv.org/abs/2211.01441 [3] Publisher https://arxiv.org/abs/2211.01441

Local Citation Graph (Related-Paper Links)

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