Work

Quantum Transfer Learning with Adversarial Robustness for Classification of High‐Resolution Image Datasets

Year: 2024

Type: article

Abstract: The application of quantum machine learning to large‐scale high‐resolution image datasets is not yet possible due to the limited number of qubits and relatively high level of noise in the current generation of quantum devices. In this work, this challenge is addressed by proposing a quantum transfer learning (QTL) architecture that integrates quantum variational circuits with a classical machine learning network pre‐trained on ImageNet dataset. Through a systematic set of simulations over a variety of image datasets such as Ants & Bees, CIFAR‐10, and Road Sign Detection, the superior performance of the QTL approach over classical and quantum machine learning without involving transfer learning is demonstrated. Furthermore, the adversarial robustness of QTL architecture with and without adversarial training is evaluated, confirming that our QTL method is adversarially robust against data manipulation attacks and outperforms classical methods.

Source: Advanced Quantum Technologies

DOI: 10.1002/qute.202400268

Authors: Amena Khatun, Muhammad Usman

Institutions: Commonwealth Scientific and Industrial Research Organisation, Data61, The University of Melbourne

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $4,220

Funders: Commonwealth Scientific and Industrial Research Organisation, National Cancer Institute