Abstract

Quantum convolutional neural network (QCNN), an early application for quantum computers in the noisy intermediate-scale quantum 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 …

Date

January 1, 1970

Authors

Aakash Ravindra Shinde, Charu Jain, Amir Kalev

Journal

Physical Review A

Volume

110

Issue

4

Pages

042409

Publisher

American Physical Society