A Novel Classical-Quantum Transfer Learning Framework for Image Recognition

Harshit Mogalapalli, Mahesh Abburi, B. Nithya +1 more

Yingxin Shan, Peng Liao, Xuyu Wang +2 more

To implement ubiquitous wireless sensing, the domain shift problem (e.g., different environments, users, devices) for machine learning based approaches should be addressed. Some existing methods are proven to be effective, such as transfer learning and domain adaptation. Meanwhile, quantum machine learning, a combination of quantum computing and machine learning, has attracted much attention. More importantly, quantum transfer learning (QTL) has been successful for certain applications, e.g., image classification. In this paper, we explore a classical to quantum (C2Q) framework to address the domain shift problem in wireless sensing by exploiting the great potential of QTL. Specifically, we first analyze the data shift problem in various types of wireless datasets by calculating the Kullback-Leibler (KL) divergence of different domains. Then, a QTL framework is designed to introduce importance weighting and adversarial strategies. We finally evaluate the proposed framework using the representative human activity recognition task on three wireless sensing datasets. Experimental results demonstrate the feasibility of the framework and its great potential for solving the domain shift problem in wireless sensing.

T. Kanimozhi, S. Sridevi, Siva Manikumar +2 more

We expand the idea of transfer learning, generally applied in current machine learning paradigm, to the evolving hybrid neural network contrived out of traditional and quantum components for brain tumor recognition from MRI images. The proposed model is a perfect synergy of traditional classical component and a revolutionary quantum component. The notion of traditional components is to customize a pre-trained network to extract features from brain tumor MRI images, whereas the notion of the quantum components is to employ variational quantum circuit to act as a classifier with learnable parameters. Exhaustive simulation experiments reveals the efficacy of the quantum transfer learning scheme to beat the performance of the conventional classical transfer learning.

Amrit Raj, Jayakumar Vaithiyashankar

Kandula Aditya Sri Krishna Raghav Chowdary, T. Janani

Shouwei Hu, Li, Xi, Banyao Ruan +1 more

The classical-quantum transfer learning (CQTL) method is introduced to address the challenge of training large-scale, high-resolution image data on a limited number of qubits (ranging from tens to hundreds) in the current Noisy Intermediate-Scale quantum (NISQ) era. existing CQTL frameworks have been demonstrate quantum advantages with a small number of parameters (around 50), but the performance of quantum neural networks is sensitive to the number of parameters. Currently, there is a lack of exploration into larger-scale quantum circuits with more parameters. This paper proposes an amplitude-encoding-based classical-quantum transfer learning (AE-CQTL) framework, accompanied by an effective learning algorithm. The AE-CQTL framework multiplies the parameters of quantum circuits by using multi-layer ansatz. Based on the AE-CQTL framework, we designed and implemented two CQTL neural network models: Transfer learning Quantum Neural Network (TLQNN) and Transfer Learning Quantum Convolutional Neural Network (TLQCNN). Both models significantly expand the parameter capacity of quantum circuits, elevating the parameter scale from a few dozen to over one hundred parameters. In cross-experiments with three benchmark datasets (MNIST, Fashion-MNIST and CIFAR10) and three source models (ResNet18, ResNet50 and DenseNet121), TLQNN and TLQCNN have exceeded the benchmark classical classifier in multiple performance metrics, including accuracy, convergence, stability, and generalization capability. Our work contributes to advancing the application of classical-quantum transfer learning on larger-scale quantum devices in future.