QGraphGAN: Quantum Graph Softmax for Adversarial Link Prediction via BFS-Tree Variational Circuits

Shoiab Shafi, Shuhul Handoo

Keywords: graph generative models, quantum machine learning, link prediction, variational quantum circuits

TL;DR: We replace each branch scorer in GraphGAN's BFS-tree generator with a small variational quantum circuit, yielding QGraphGAN — a principled quantum analogue of GraphGAN that isolates the quantum contribution to graph-structured generation.

Abstract: GraphGAN generates graph neighbors by walking a BFS tree, scoring each branch with a classical softmax. We replace each branch scorer with a small variational quantum circuit — a parameterized circuit that uses quantum superposition to produce branching probabilities — yielding QGraphGAN. A learned STOP action lets the generator terminate at any non-root node, preventing exponential probability suppression of deep-tree paths. We pair the quantum generator with GraphGAN's original dot-product discriminator, isolating the quantum claim to the generative distribution. Training uses REINFORCE with baseline subtraction. Two independent GraphSAGE encoders provide separate generator and discriminator embeddings. Evaluated over three seeds on Karate Club (34 nodes), SBM (32 nodes), and a CA-GrQc subgraph, QGraphGAN outperforms the classical BFS-GAN by +0.08 to +0.32 AUC across datasets. On real-world-like topology it matches DeepWalk and Adamic–Adar; on clean synthetic structure (SBM) classical heuristics dominate — an honest negative result we report in full.

Track: Track 2: ML Research by Muslim Authors

Email Sharing: We authorize the sharing of all author emails with Program Chairs.

Data Release: We authorize the release of our submission and author names to the public in the event of acceptance.

Non Archival Confirmation: I understand that submissions to MusIML are non-archival and can be submitted to other venues.

Submission Number: 33