量子卷积网络提升光谱峰检测，性能优于传统方法。
Quanvolutional Neural Networks for Spectrum Peak-Finding
- 用量子卷积网络处理多任务峰检测问题。
- 峰定位误差降低30%，F1分数提升11%。
- 更适合复杂分子光谱分析，收敛更稳定。
核磁共振（NMR）等光谱的峰分析是专家和机器共同面临的挑战，尤其在复杂分子中，该过程称为去卷积，需识别并量化谱峰。机器学习已展现自动化潜力。随着量子计算的发展，进一步提升此类技术成为可能。本文受经典卷积神经网络（CNN）成功启发，探索量子卷积神经网络（QuanvNNs）在多任务峰检测中的应用，包括峰计数与位置估计。我们设计了一种简单且可解释的QuanvNN架构，可与经典CNN直接对比，并在合成的类NMR数据集上评估其性能。结果表明，对于复杂谱图，QuanvNNs优于经典CNN，F1分数提升11%，峰位置估计的平均绝对误差降低30%。此外，QuanvNNs在困难问题上表现出更好的收敛稳定性。
原文摘要 · Abstract (English)
The analysis of spectra, such as Nuclear Magnetic Resonance (NMR) spectra, for the comprehensive characterization of peaks is a challenging task for both experts and machines, especially with complex molecules. This process, also known as deconvolution, involves identifying and quantifying the peaks in the spectrum. Machine learning techniques have shown promising results in automating this process. With the advent of quantum computing, there is potential to further enhance these techniques. In this work, inspired by the success of classical Convolutional Neural Networks (CNNs), we explore the use of Quanvolutional Neural Networks (QuanvNNs) for the multi-task peak finding problem, involving both peak counting and position estimation. We implement a simple and interpretable QuanvNN architecture that can be directly compared to its classical CNN counterpart, and evaluate its performance on a synthetic NMR-inspired dataset. Our results demonstrate that QuanvNNs outperform classical CNNs on challenging spectra, achieving an 11\% improvement in F1 score and a 30\% reduction in mean absolute error for peak position estimation. Additionally, QuanvNNs appear to exhibit better convergence stability for harder problems.
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