3
first-author 2026 acceptances
ICML · ECCV · IEEE QCE
Available for 2027 research, faculty, and advanced R&D opportunities.
Luke James Miller · Ph.D. Candidate in Computer Science
Graph-theoretic representations for quantum computing and machine learning.
I study how structural information is encoded, compressed, and recovered—and turn that understanding into practical algorithms for quantum circuits, graph learning, and scientific computing.
4.94/5
quantum-computing course rating
UMKC graduate instruction
12,000+
hours of technical instruction
United States Air Force
76
Qiskit Fall Fest participants
21 technical sessions
Research program
One question across quantum computing and machine learning
My work treats representations as objects to be analyzed, not merely used. I ask what structure they preserve, how that structure can be recovered, and what computation becomes possible after the representation changes.
Quantum computing
Quantum representations of graph structure
I develop shallow quantum circuits that encode graph structure without requiring variational training, then study what graph information their measurement distributions retain.
- Training-free quantum graph embedding for graph-invariant construction.
- Structured readout designed for truncation and finite-shot measurement.
- Accepted for oral presentation at IEEE Quantum Week 2026.
Computational graph theory
Structural accessibility under finite resources
My current theoretical work separates ideal structural signal from decoder conditioning, truncation loss, sampling error, and hardware effects.
- Connects graph motifs to explicit circuit and readout behavior.
- Treats finite-shot recovery as a resource question rather than an afterthought.
- Provides a mathematical foundation for practical quantum graph representations.
Machine learning
Graph-minor representations for efficient learning
I replace large dense prediction domains with compact, topology-aware graph structures that preserve the support needed for downstream learning.
- First-author work accepted at ICML 2026 and ECCV 2026.
- Applied to volumetric and thin-structure segmentation.
- Unifies graph construction, representation reduction, and downstream learning.
Experience and impact
Research output backed by teaching, mentorship, and technical leadership
The publication record is supported by substantial experience in course design, technical instruction, student mentorship, community building, and interdisciplinary research execution.
Research
Research across quantum graph algorithms, graph representation learning, scientific machine learning, and real-time signal classification.
$3MNSF NRT program
QCE2026 oral paper
DARPAWARDEN contribution
Teaching & mentorship
Course design, graduate instruction, technical training, and research mentorship from introductory audiences through advanced quantum computing.
4.94/5course rating
10REU mentees
2mentee publications
Leadership & outreach
Technical-community leadership spanning IEEE, Eta Kappa Nu, quantum-computing outreach, and large student-facing events.
64IEEE members
62quantum society members
76Fall Fest participants
Operational foundation
Technical communication under real constraints
Before graduate research, I spent nine years as a United States Air Force air-traffic-control instructor. That background contributes directly to how I approach research: define the system precisely, communicate difficult material clearly, and build methods that survive practical constraints rather than only ideal analysis.
Selected publications
Recent first-author work
Accepted work spans quantum graph representations and graph-minor learning, with both theoretical and hardware-facing evaluation.
QuIC: A Training-Free Quantum Graph Embedding from Ideal Analysis to Practical Hardware Evaluation
L. J. Miller and Y. Lee
Oral presentation
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
Contact
Research, faculty, and advanced R&D opportunities
I am based in Kansas City and available for conversations about research roles, collaborations, invited talks, and technical education.