Overfitting in quantum machine learning and entangling dropout

Abstract

The ultimate goal in machine learning is to construct a model function that has a generalization capability for unseen dataset, based on given training dataset. If the model function has too much expressibility power, then it may overfit to the training data and as a result lose the generalization capability. To avoid such overfitting issue, several techniques have been developed in the classical machine learning regime, and the dropout is one such effective method. This paper proposes a straightforward analogue of this technique in the quantum machine learning regime, the entangling dropout, meaning that some entangling gates in a given parametrized quantum circuit are randomly removed during the training process to reduce the expressibility of the circuit. Some simple case studies are given to show that this technique actually suppresses the overfitting.

7 pages, 8 figures

Cited by in corpus (8)

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• Towards Improved Quantum Machine Learning for Molecular Force Fields
• Double descent in quantum kernel methods
• Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern