Transfer Learning Based Hybrid Quantum Neural Network Model for Surface Anomaly Detection

2024/08/30 by Sounak Bhowmik, Bhowmik, Sounak, Himanshu Thapliyal +1 · 3 citations
Computer Science · #Anomaly Detection Techniques and Applications #FOS: Physical sciences #Quantum Physics (quant-ph)

paper · pdf · doi:10.48550/arxiv.2409.00228

openalex publication_date 2024/08/30 · openalex created_date 2024/09/29 · openalex updated_date 2026/07/28

Abstract

The rapid increase in the volume of data increased the size and complexity of the deep learning models. These models are now more resource-intensive and time-consuming for training than ever. This paper presents a quantum transfer learning (QTL) based approach to significantly reduce the number of parameters of the classical models without compromising their performance, sometimes even improving it. Reducing the number of parameters reduces overfitting problems and training time and increases the models' flexibility and speed of response. For illustration, we have selected a surface anomaly detection problem to show that we can replace the resource-intensive and less flexible anomaly detection system (ADS) with a quantum transfer learning-based hybrid model to address the frequent emergence of new anomalies better. We showed that we could reduce the total number of trainable parameters up to 90% of the initial model without any drop in performance.

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