# Quanvolutional Neural Networks for Spectrum Peak-Finding

arXiv: https://arxiv.org/abs/2512.13125 · HTML: https://mlsanity.com/paper/2512.13125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 47 references that are in the map.
- First posted on arXiv 15 Dec 2025.

## Builds on (most cited first, 6 of 6)

- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Barren plateaus in quantum neural network training landscapes](https://mlsanity.com/paper/1803.11173/) (2018)
- [Quantum advantage in learning from experiments](https://mlsanity.com/paper/2112.00778/) (2021)
- [Review and Prospect: Deep Learning in Nuclear Magnetic Resonance Spectroscopy](https://mlsanity.com/paper/2001.04813/) (2020)
- [Applying Quantum Autoencoders for Time Series Anomaly Detection](https://mlsanity.com/paper/2410.04154/) (2024)
- [Quanv4EO: Empowering Earth Observation by means of Quanvolutional Neural Networks](https://mlsanity.com/paper/2407.17108/) (2024)

## Abstract (arXiv)

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.