An Amplitude-Encoding-Based Classical-Quantum Transfer Learning framework: Outperforming Classical Methods in Image Recognition

• URL: http://arxiv.org/abs/2502.20184v1
• Date: Thu, 27 Feb 2025 15:20:01 GMT
• Title: An Amplitude-Encoding-Based Classical-Quantum Transfer Learning framework: Outperforming Classical Methods in Image Recognition
• Authors: Shouwei Hu, Xi Li, Banyao Ruan, Zhihao Liu,
• Abstract summary: This paper proposes an amplitude-encoding-based classical-quantum transfer learning (AE-CQTL) framework, accompanied by an effective learning algorithm.<n>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)
• Score: 8.971481563534537
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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