利用量子近似优化算法增强卷积自编码器以实现图像降噪
Enhancing a Convolutional Autoencoder with a Quantum Approximate Optimization Algorithm for Image Noise Reduction
AI总结：
提出用QAOA启发的量子潜在空间增强卷积自编码器的QCAE方法，并借助参数移位规则在真实量子计算机上优化学习，实验表明其去噪SSIM最高提升40%。
摘要：
图像去噪对于消除图像采集过程中由电子设备故障或其他因素引起的图像噪声至关重要。它可确保图像质量得到保持并实现准确判读。许多卷积自编码器算法已被证明在图像去噪中行之有效。由于量子计算机具有可观的效率前景，其已受到广泛关注。本文提出一种量子卷积自编码器（QCAE）方法，通过用由受QAOA启发的ansatz电路实现的量子对应部分替换传统卷积自编码器的潜在空间来增强后者。为提升效率，我们利用结合量子近似优化算法（QAOA）的参数移位规则的优势来确定优化的代价函数，从而促进在真实量子计算机上从数据中进行有效学习和梯度计算。所提出的QCAE方法表现出更低的训练损失和更高的结构相似性指数（SSIM）值，优于其经典对应方法。在MNIST数据集去噪方面，QCAE的SSIM值较其经典对应方法最高高出40%，证实了其在实际应用中的增强能力。对不同电路配置和层数变化下QAOA性能的评估表明，我们的技术平均比其他电路设计高出25%。
英文摘要：
Image denoising is essential for removing noise in images caused by electric device malfunctions or other factors during image acquisition. It ensures the preservation of image quality and accurate interpretation. Many convolutional autoencoder algorithms have proven effective in image denoising. Owing to their promising efficiency, quantum computers have gained popularity. This paper proposes a method, the quantum convolutional autoencoder (QCAE), which enhances traditional convolutional autoencoders by replacing their latent space with a quantum counterpart implemented via a QAOA-inspired ansatz circuit. To enhance efficiency, we leveraged the advantages of the quantum approximate optimization algorithm (QAOA)-incorporated parameter-shift rule to identify an optimized cost function, facilitating effective learning from data and gradient computation on an actual quantum computer. The proposed QCAE method outperformed its classical counterpart as it exhibited lower training loss and a higher structural similarity index (SSIM) value. QCAE also outperformed its classical counterpart in denoising the MNIST dataset by up to 40% in terms of SSIM value, confirming its enhanced capabilities in real-world applications. Evaluation of QAOA performance across different circuit configurations and layer variations showed that our technique outperformed other circuit designs by 25% on average.
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