# Exploring polymer classification with a hybrid single-photon quantum approach

arXiv: https://arxiv.org/abs/2512.18125 · HTML: https://mlsanity.com/paper/2512.18125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 57 references that are in the map.
- First posted on arXiv 19 Dec 2025.

## Builds on (most cited first, 9 of 9)

- [Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network](https://mlsanity.com/paper/1808.03314/) (2018)
- [Hierarchical Generation of Molecular Graphs using Structural Motifs](https://mlsanity.com/paper/2002.03230/) (2020)
- [Supervised quantum machine learning models are kernel methods](https://mlsanity.com/paper/2101.11020/) (2021)
- [Transfer learning in hybrid classical-quantum neural networks](https://mlsanity.com/paper/1912.08278/) (2019)
- [Message-passing neural networks for high-throughput polymer screening](https://mlsanity.com/paper/1807.10363/) (2018)
- [Data-Efficient Graph Grammar Learning for Molecular Generation](https://mlsanity.com/paper/2203.08031/) (2022)
- [Fock State-enhanced Expressivity of Quantum Machine Learning Models](https://mlsanity.com/paper/2107.05224/) (2021)
- [Graph Convolutional Neural Networks for Polymers Property Prediction](https://mlsanity.com/paper/1811.06231/) (2018)
- [Polygrammar: Grammar for Digital Polymer Representation and Generation](https://mlsanity.com/paper/2105.05278/) (2021)

## Abstract (arXiv)

Polymers exhibit complex architectures and diverse properties that place them at the center of contemporary research in chemistry and materials science. As conventional computational techniques, even multi-scale ones, struggle to capture this complexity, quantum computing offers a promising alternative framework for extracting structure-property relationships. Noisy Intermediate-Scale Quantum (NISQ) devices are commonly used to explore the implementation of algorithms, including quantum neural networks for classification tasks, despite ongoing debate regarding their practical impact. We present a hybrid classical-quantum formalism that couples a classical deep neural network for polymer featurization with a single-photon-based quantum classifier native to photonic quantum computing. This pipeline successfully classifies polymer species by their optical gap, with performance in line between CPU-based noisy simulations and a proof-of-principle run on Quandela's Ascella quantum processor. These findings demonstrate the effectiveness of the proposed computational workflow and indicate that chemistryfrelated classification tasks can already be tackled under the constraints of today's NISQ devices.