Classifying, auto-encoding and reverse-engineering QUBO matrices

Applied quantum kernels for anomaly detection. Low-data anomaly detection on manifold-structured telemetry, benchmarking entanglement kernels vs classical baselines with geometric diagnostics.

Foundations of quantum representation. Expressivity and geometry analysis of quantum kernels using PennyLane and PyTorch, establishing when/how quantum feature maps differ from classical baselines.

Recursive law learning under measurement constraints. A falsifiable SQNT-inspired testbed for autodidactic rules: internalizing structure under measurement invariants and limited observability.

AI/ML Graduate Student @ ASU | Scientific Developer @ Cadence | Specializing in GenAI, CUDA, Protein Modeling & Deep Learning

A minimal hybrid Quantum–Graph Neural Network prototype

Repository untuk eksperimen dan latihan Quantum Machine Learning

🧬 Build and explore a minimal Quantum-Graph Neural Network for node classification, combining classical encoders with quantum circuits for enhanced insights.

🛰 Enhance quantum telemetry analysis by detecting anomalies in quantum-kernel geometry with this reproducible framework for insightful research.

Hybrid quantum-classical machine learning framework that runs on real quantum computers - bridge between quantum computing and AI.

🤖 Explore advanced AI and machine learning solutions for protein modeling and medical applications, developed by a dedicated data science graduate student.