Superpositional Quantum Network Topologies
Frontier AI research through rigorous systems-level experimentation
Applying physics-inspired experimental methods and information-theoretic tools to measure scaling, robustness, and alignment properties of frontier AI systems.
Our research program builds falsification frameworks for frontier models and AI alignment claims, with particular focus on local recoverability assumptions in neural network editing. We combine quantum machine learning methods with classical testbeds to probe function–representation decoupling, continuation interests, and the reliability of geometric proxies in model repair.
Current work spans quantum kernel methods for satellite anomaly detection, binarized quantum neural networks, Hamiltonian dynamics for data augmentation, satellite quantum key distribution, and superconducting qubit simulation—each project packaged as a reproducible evaluation harness with diagnostic plots, baselines, and reviewable artifacts designed to test specific technical claims under controlled experimental conditions.
Collaborations
This live codebase reflects active research collaborations and coauthored work across AI alignment, quantum machine learning, and superconducting circuits. Repositories are maintained as reproducible artifacts; credit for specific contributions appears in papers, commit history, and project documentation.
Coauthorship
Research claims are anchored in publishable units: falsifiable hypotheses, experimental protocols, and measurable outcomes.
- Paper-grade methods sections and result traces
- Clear provenance: what changed, why, and what it implies
- Attribution in manuscripts and repo history
Joint Codebases
Projects are structured so collaborators can reproduce, extend, or refute results with minimal friction.
- Deterministic runs (seeds, artifacts, locked protocols)
- Comparable baselines and diagnostic plots
- Small, reviewable increments over time
Ongoing Threads
The emphasis is disciplined iteration: refine the claim, tighten the test, and keep the artifact honest.
- Alignment falsification testbeds
- Quantum kernels + telemetry/anomaly regimes
- Device-oriented superconducting qubit studies
Collaboration inquiries: [email protected]
Selected Publications
Backpropagation in Adaptive Quantum Networks
Spacetime from Quantum Topology
Accelerated Training Convergence in Superposed Quantum Networks
Astronaut Development and Deployment of a Secure Space Communications Network
Quantum Information Science and Technology Project, Asian Technology Information Program, Tokyo.
Selected Results
+6.2%
BQNN parity advantage vs classical
ρ ≈ 0.04
Accuracy decouples from recoverability
CI ≈ 0
Continuation interest post-recovery
