基于散斑图案与量子网络参数联合优化的量子增强鬼成像识别
Quantum-enhanced ghost imaging recognition via joint optimization of speckle patterns and quantum network parameters
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中文总结 AI 辅助
本文提出一种散斑图案与量子网络参数联合优化的鬼成像识别方法，在MNIST等数据集超低采样率下实现更高识别准确率，且抗噪性强，为低采样鬼成像识别提供了可靠方案。
中文摘要 AI 辅助
鬼成像可实现非局域图像重建，且对干扰具有较强鲁棒性，但在超低采样率下实现高保真识别仍具挑战性。量子机器学习为含噪中等规模量子设备的高效特征提取提供了新方法，不过现有方法普遍存在识别准确率低、抗噪性弱的问题。本文提出一种基于散斑图案与量子网络参数同时优化的鬼成像识别方法，利用鬼成像中经典卷积与散斑-目标点积运算的数学等价性，引入散斑一致性正则化机制，实现光学编码与量子特征提取器的端到端联合优化。设计采用块编码与星形纠缠结构的并行8量子比特量子电路，从桶探测器信号中提取高阶特征。在MNIST与Fashion-MNIST数据集上的仿真结果显示，在1.5625%的超低采样率下，该框架分别实现90.1%与81.7%的识别准确率，较经典卷积神经网络提升2.6%，较传统混合量子机器学习模型最高提升14.2%。该方法还表现出较强的量子噪声鲁棒性，已在实际光学鬼成像系统中得到验证，平均识别准确率达84.8%。这些结果证实，散斑图案与量子网络参数的联合优化为低采样率鬼成像识别提供了可靠且实用的解决方案。
英文摘要
Ghost imaging enables nonlocal image reconstruction and exhibits strong robustness against interference, but achieving high-fidelity recognition at ultra-low sampling rates remains challenging. Quantum machine learning offers a novel approach for efficient feature extraction on noisy medium-scale quantum devices; however, existing methods generally suffer from low recognition accuracy and weak noise resistance. This paper proposes a ghost imaging recognition method based on the simultaneous optimization of speckle patterns and quantum network parameters. By leveraging the mathematical equivalence between classical convolution and speckle-object dot product operations in ghost imaging, a speckle consistency regularization mechanism is introduced to achieve end-to-end joint optimization of optical coding and quantum feature extractors. A parallel 8-qubit quantum circuit employing block coding and a star-shaped entanglement structure is designed to extract higher-order features from bucket signals. Simulation results on the MNIST and Fashion-MNIST datasets show that at an ultra-low sampling rate of 1.5625%, the proposed framework achieves recognition accuracies of 90.1% and 81.7%, respectively, representing a 2.6% improvement over classical convolutional neural networks and a maximum improvement of 14.2% over traditional hybrid quantum machine learning models. This method also exhibits strong robustness to quantum noise and has been validated on a real optical ghost imaging system, achieving an average recognition accuracy of 84.8%. These results confirm that the joint optimization of speckle patterns and quantum network parameters provides a reliable and practical solution for low-sampling ghost imaging recognition.
