arXiv Machine Learning By Nouhaila Innan, M. Murali Karthick, Simeon Kandan Sonar, Vivek Chaturvedi, Muhammad Shafique

A2QTGN: Adaptive Amplitude Quantum-Integrated Temporal Graph Network for Dynamic Link Prediction

Read the original on arXiv Machine Learning →

arXiv:2605. 21916v2 Announce Type: replace-cross Abstract: Dynamic link prediction is important for modeling evolving interactions in social, communication, financial, and transportation networks.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

arXiv Machine Learning
Sep 18

Quantum Graph Convolutional Networks: Implementation and Trainability Analysis

The paper implements two quantum graph neural network architectures—Simplified Graph Convolution (SGC) and Linear Graph Convolution (LGC)—and evaluates them on benchmark graph datasets for semi‑supervised learning using quantum simulation. It compares their predictive performance and optimization behavior to classical baselines, finding that the quantum models achieve competitive results with fewer parameters. Additionally, the study provides a cost‑gradient analysis to identify trainable tasks and a classical simulability investigation to determine regimes where the circuits remain robust during training.

By Paul San Sebastian Sein, Theodor Iosif, Tilen G. Limb\"ack-Stokin, Kin Ian Lo, Yidong Liao