3 papers
quant-ph2026
Learning structural balance of graphs from quantum spectral features
Stefano Scali, Oleksandr Kyriienko
We develop a quantum approach to spectral feature extraction from the density of states (DOS) of a problem-dependent Hamiltonian, and apply it to machine learning on signed graphs.…
physics.optics2025
Photonics-Enhanced Graph Convolutional Networks
Yuan Wang, Oleksandr Kyriienko
Photonics can offer a hardware-native route for machine learning (ML). However, efficient deployment of photonics-enhanced ML requires hybrid workflows that integrate optical proce…
cond-mat.dis-nn2025
Polaritonic Machine Learning for Graph-based Data Analysis
Yuan Wang, Stefano Scali, Oleksandr Kyriienko
Photonic and polaritonic systems offer a fast and efficient platform for accelerating machine learning (ML) through physics-based computing. To gain a computational advantage, howe…