Quanvolutional Neural Networks for Spectrum Peak-Finding
This addresses the challenge of analyzing complex spectra for experts and machines, though it is incremental as it adapts quantum-inspired methods to a specific domain.
The paper tackles the problem of automating peak-finding in spectra, such as NMR, by using Quanvolutional Neural Networks (QuanvNNs), which outperform classical CNNs with an 11% improvement in F1 score and a 30% reduction in mean absolute error for peak position estimation.
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.