QuIC: A Training-Free Quantum Graph Embedding from Ideal Analysis to Practical Hardware Evaluation
L. J. Miller and Y. Lee
Oral presentation
Publications
Quantum graph representations, graph-minor learning, and structured computation.
The publication record centers on representations of graph-structured data, with work spanning quantum circuits, graph invariants, segmentation, optimization, and scientific applications.
Accepted and published
Peer-reviewed work
The first three entries are first-author papers accepted in 2026 at IEEE Quantum Week, ICML, and ECCV.
SEMIR: Semantic Minor-Induced Representation Learning for Visual Segmentation
L. J. Miller and Y. Lee
SEMIR: Topology-Preserving Graph Minors for Thin-Structure Segmentation
L. J. Miller and Y. Lee
Trust-Aware Task Allocation with Quantum Optimization
G. Roman-Barroso, D. Ho, L. J. Miller et al.
Resilient Multi-Agent Task Allocation Through Trust-Aware Quantum-Inspired Optimization
J. Ko, J. Stiles, A. Alanazi, L. J. Miller, D. Ho, and Y. Lee
pp. 2726–2735
Detecting Pilot Task Saturation
N. Hofer, L. J. Miller et al.
Manuscripts
Under review
Submission statuses are current as of August 2026 and will be updated as decisions or revised titles become public.
QuIC-Mol: A Training-Free Quantum Molecular Embedding for Hybrid Machine Learning
L. J. Miller and Y. Lee
Quantum Graph Neural Networks: A Survey
L. J. Miller and Y. Lee
Support-Carrying Minor Representations for Dense Prediction
L. J. Miller and Y. Lee
Efficient Clinical Minority-Structure Segmentation via Target-Conditioned Inference-Domain Reduction
L. J. Miller and Y. Lee
Papers and code