On the explainability of quantum neural networks based on variational quantum circuits
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Abstract (arXiv)
Ridge functions are used to describe and study the lower bound of the approximation done by the neural networks which can be written as a linear combination of activation functions. If the activation functions are also ridge functions, these networks are called explainable neural networks. In this brief paper, we first show that quantum neural networks which are based on variational quantum circuits can be written as a linear combination of ridge functions by following matrix notations. Consequently, we show that the interpretability and explainability of such quantum neural networks can be directly considered and studied as an approximation with the linear combination of ridge functions.
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References · 10
- 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
- 2019A Survey on Explainable Artificial Intelligence (XAI): Towards Medical XAIcited by 403
- 2020The Kolmogorov-Arnold representation theorem revisitedcited by 89
- 2020Universal Approximation Property of Quantum Machine Learning Models in Quantum-Enhanced Feature Spacescited by 44
- 2018Explainable Neural Networks based on Additive Index Modelscited by 33
- 2021Quantum Neural Networks: Concepts, Applications, and Challengescited by 21
- 2021Fock State-enhanced Expressivity of Quantum Machine Learning Modelscited by 11
- 2021A Leap among Quantum Computing and Quantum Neural Networks: A Surveycited by 8
- 2018A Simple Quantum Neural Net with a Periodic Activation Functioncited by 7
- 2017Minimax Lower Bounds for Ridge Combinations Including Neural Netscited by 4
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