Foundations & Theory
Establishes limits, resource tradeoffs, and information-theoretic questions that guide the program.
My research asks how rigorous quantum theory can become reliable, verifiable, and useful computation, moving from fundamental limits to systems and workflows that can be tested in practice.
The four research areas form a cycle: foundations define limits, hardware implements reliable systems, software computes under constraints, and applications feed new questions back.
The shared target of the cycle.
Establishes limits, resource tradeoffs, and information-theoretic questions that guide the program.
Turns those limits into architectures, error-correcting structures, and benchmarks for reliable quantum computation.
Builds algorithms, learning protocols, and verification tools that operate under realistic system constraints.
Uses domain workflows to test what is useful, expose bottlenecks, and send new constraints back into the program.
These areas organize the group's work from enabling hardware-level reliability to software advantage, real-world workflows, and the mathematical foundations underneath them.
Exploring the fundamental limits, information-theoretic principles, and physical structures that make quantum information processing possible.
Explore this areaBuilding the physical and architectural foundation for reliable, scalable quantum computation through error correction, benchmarking, and system-aware design.
Explore this areaTranslating quantum capabilities into practical workflows for simulation, sensing, communication, and domain problems where structure matters.
Explore this areaDeveloping algorithms, quantum learning protocols, and verification tools that turn quantum resources into measurable computational advantages.
Explore this areaA few representative threads show how the work moves between rigorous theory, algorithms, and practical quantum systems.
Designing quantum error-correcting codes that bring efficient encoding and decoding closer to fault-tolerant architectures.
Connecting code constructions with locality constraints that matter for scalable physical implementations.
Using modern AI tools to model, represent, and understand quantum states and processes at useful scales.
A compact entry point into the publication record, with emphasis on active themes across fault tolerance, learning, and quantum information.