Yogaraj Kavitha, Quanz Brian, Vikas Tarun, Mondal Arijit, Mondal Samrat
IBM Quantum, IBM Research, Bengaluru, India.
Department of Computer Science and Engineering, Indian Institute of Technology, Patna, India.
Sci Rep. 2025 Jul 2;15(1):23682. doi: 10.1038/s41598-025-08887-2.
We address the limitations of variational quantum circuits (VQCs) in hybrid classical-quantum transfer learning by introducing post-variational strategies, which reduce training overhead and mitigate optimization issues. Our approach Post Variational Classical Quantum Transfer Learning (PVCQTL) includes three designs: (1) modified observable construction, (2) a hybrid approach, and (3) a variational-post-variational combination. We evaluate these on pre-trained models (VGG19, ResNet50, ResNet18, MobileNet) for 4 and 8 qubits, with ResNet50 performing best in deepfake detection. Compared to classical models (MLP, ResNet50) and quantum baselines hybrid quantum classical neural network (HQCNN), classical-quantum transfer learning (CQTL). PVCQTL consistently achieves better accuracy. The modified observable variant reaches 85% accuracy for Deepfake dataset with lower computational cost. To evaluate generalizability, we tested PVCQTL on three additional binary classification datasets, observing improved accuracy on each. We conducted ablation studies to assess the effects of architectural choices on quantum component variations, including the choice of quantum gates, use of fixed ansatz circuits, and observable measurements. Robustness to input noise and sensitivity of the PVCQTL models were examined through ablation studies on learning rate, batch size, and number of qubits. These results demonstrate that PVCQTL offers a measurable improvement over traditional hybrid classical-quantum approaches.
我们通过引入变分后策略来解决混合经典 - 量子迁移学习中变分量子电路（VQC）的局限性，这些策略可减少训练开销并缓解优化问题。我们的方法——变分后经典量子迁移学习（PVCQTL）包括三种设计：（1）修改可观测量构造，（2）一种混合方法，以及（3）变分 - 变分后组合。我们在预训练模型（VGG19、ResNet50、ResNet18、MobileNet）上针对4个和8个量子比特对这些方法进行了评估，其中ResNet50在深度伪造检测中表现最佳。与经典模型（MLP、ResNet50）和量子基线混合量子经典神经网络（HQCNN）、经典 - 量子迁移学习（CQTL）相比，PVCQTL始终能实现更高的准确率。修改后的可观测量变体在深度伪造数据集上以较低的计算成本达到了85%的准确率。为了评估通用性，我们在另外三个二元分类数据集上测试了PVCQTL，在每个数据集上都观察到了准确率的提高。我们进行了消融研究，以评估架构选择对量子组件变化的影响，包括量子门的选择、固定量子电路模板的使用以及可观测量测量。通过对学习率、批量大小和量子比特数量的消融研究，检验了PVCQTL模型对输入噪声的鲁棒性和敏感性。这些结果表明，PVCQTL相对于传统的混合经典 - 量子方法有可衡量的改进。
