Quantum Evolution Kernel for Node-Attributed Graphs
Abstract
We present a novel approach to embed node-attributed graphs onto neutral-atom quantum computers, and propose an application to digit recognition. Our approach extends PASQAL's quantum evolution kernel (QEK) by introducing a node-level feature encoding based on three-level quantum states. To this purpose, we present a prototype showing how the resulting embedding can be leveraged to extract correlation-based information for classification tasks. The entire quantum process is simulated using Pasqal's Pulser library, which inevitably imposes constraints on the size of the experimental datasets. We compare our method with classical baselines to evaluate its potential. We show that, for the digit recognition task, our method achieves a higher mean accuracy than the more commonly used Jensen-Shannon distance-based methods by utilizing the Wasserstein distance.
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BibTeX
@misc{padovani2026quantum,
title = {{Quantum Evolution Kernel for Node-Attributed Graphs}},
author = {Manuel Padovani and Alessandra Di Pierro and Claudia Daffara},
howpublished = {arXiv},
year = {2026},
month = oct,
doi = {10.48550/arxiv.2610.36984},
eprint = {2610.36984},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2610.36984},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Quantum Evolution Kernel for Node-Attributed Graphs
AU - Manuel Padovani
AU - Alessandra Di Pierro
AU - Claudia Daffara
PY - 2026
DA - 2026/10/27
PB - arXiv
DO - 10.48550/arxiv.2610.36984
UR - https://arxiv.org/abs/2610.36984
AB - We present a novel approach to embed node-attributed graphs onto neutral-atom quantum computers, and propose an application to digit recognition. Our approach extends PASQAL's quantum evolution kernel (QEK) by introducing a node-level feature encoding based on three-level quantum states. To this purpose, we present a prototype showing how the resulting embedding can be leveraged to extract correlation-based information for classification tasks. The entire quantum process is simulated using Pasqal's Pulser library, which inevitably imposes constraints on the size of the experimental datasets. We compare our method with classical baselines to evaluate its potential. We show that, for the digit recognition task, our method achieves a higher mean accuracy than the more commonly used Jensen-Shannon distance-based methods by utilizing the Wasserstein distance.
ER -