# A Post-Training Approach for Mitigating Overfitting in Quantum Convolutional Neural Networks

arXiv: https://arxiv.org/abs/2309.01829 · HTML: https://mlsanity.com/paper/2309.01829/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 9 of 9)

- [Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning](https://mlsanity.com/paper/1506.02142/) (2015)
- [Parameterized quantum circuits as machine learning models](https://mlsanity.com/paper/1906.07682/) (2019)
- [Entangled Datasets for Quantum Machine Learning](https://mlsanity.com/paper/2109.03400/) (2021)
- [A divide-and-conquer algorithm for quantum state preparation](https://mlsanity.com/paper/2008.01511/) (2020)
- [Classification with Quantum Machine Learning: A Survey](https://mlsanity.com/paper/2006.12270/) (2020)
- [Practical application-specific advantage through hybrid quantum computing](https://mlsanity.com/paper/2205.04858/) (2022)
- [Overfitting in quantum machine learning and entangling dropout](https://mlsanity.com/paper/2205.11446/) (2022)
- [A preprocessing perspective for quantum machine learning classification advantage using NISQ algorithms](https://mlsanity.com/paper/2208.13251/) (2022)
- [Predict better with less training data using a QNN](https://mlsanity.com/paper/2206.03960/) (2022)

## Built on by (most cited first, 1 of 1)

- [Influence of Data Dimensionality Reduction Methods on the Effectiveness of Quantum Machine Learning Models](https://mlsanity.com/paper/2511.03320/) (2025)

## Abstract (arXiv)

Quantum convolutional neural network (QCNN), an early application for quantum computers in the NISQ era, has been consistently proven successful as a machine learning (ML) algorithm for several tasks with significant accuracy. Derived from its classical counterpart, QCNN is prone to overfitting. Overfitting is a typical shortcoming of ML models that are trained too closely to the availed training dataset and perform relatively poorly on unseen datasets for a similar problem. In this work we study post-training approaches for mitigating overfitting in QCNNs. We find that a straightforward adaptation of a classical post-training method, known as neuron dropout, to the quantum setting leads to a significant and undesirable consequence: a substantial decrease in success probability of the QCNN. We argue that this effect exposes the crucial role of entanglement in QCNNs and the vulnerability of QCNNs to entanglement loss. Hence, we propose a parameter adaptation method as an alternative method. Our method is computationally efficient and is found to successfully handle overfitting in the test cases.