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.

arXiv comment7 pages, 8 figures

References in corpus (9)

• Quantum Machine Learning
• Supervised learning with quantum enhanced feature spaces
• Quantum Circuit Learning
• Circuit-centric quantum classifiers
• The effect of data encoding on the expressive power of variational quantum machine learning models
• Data re-uploading for a universal quantum classifier
• Generalization in quantum machine learning from few training data
• Generalization in Quantum Machine Learning: a Quantum Information Perspective
• Reformulation of the No-Free-Lunch Theorem for Entangled Data Sets

Cited by in corpus (8)

• A General Approach to Dropout in Quantum Neural Networks
• A Post-Training Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks
• Permutation-equivariant quantum convolutional neural networks
• Learning Fourier series with parametrized quantum circuits
• Graph Neural Networks for Parameterized Quantum Circuits Expressibility Estimation
• 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