Explaining Graph Machine Learning Models via Tensor Networks: A Bridge to Quantum Computing

This research investigates whether tensor networks can serve as interpretable surrogates for graph neural networks (GNNs). It explores whether tensor networks can approximate the functional behavior of GNNs while offering a more structured and interpretable internal representation. The project aims to quantify the contribution of nodes, edges, and features to predictions through this surrogate representation, enhancing model transparency. It also examines how the extracted tensor structure could inform the design of efficient quantum circuits, leveraging the deep mathematical connections between tensor networks and quantum computation.

Tensor networks are compact, modular, and inherently structured—traits that make them promising candidates for interpretable machine learning. Their alignment with quantum circuit models allows not only for a clearer understanding of classical GNNs but also for porting learned structures into quantum-native architectures. This bridges a key gap between explainability in graph machine learning and practical quantum algorithm design.

By combining RIKEN AIP’s expertise in quantum computing and interpretability with University of Montreal and Mila’s strengths in graph models and machine learning, this collaboration creates a unique opportunity to advance interdisciplinary research. Mila gains exposure to advanced quantum approaches, while RIKEN AIP benefits from insights into graph-based AI, enabling new directions in tensor-based and quantum-inspired model development.

Faculty Supervisor:

Guillaume Rabusseau

Student:

Partner:

RIKEN (Center for Advanced Intelligence Project)

Discipline:

Computer science

Sector:

Quantum Science; Artificial Intelligence

University:

Université de Montréal

Program: