Classifying, auto-encoding and reverse-engineering QUBO matrices

Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible “Wigner’s friend” branch-transfer coherence-witness experiments executed on superconducting quantum hardware.

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

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

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.

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

Open Source Python library for Quantum Data Encodings in Quantum ML - multi-framework support, analysis tools

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

Quantum Representation Learning with contrastive SWAP test using TensorFlow Quantum and Cirq.

Quantum Graph Neural Network (QGNN) implementation with Cirq to encode graph-structured data into quantum circuits.

Quantum-classical ML interface — bridging quantum computing and machine learning

My fork of Qiskit — exploring quantum computing algorithms and hybrid classical-quantum ML approaches. Personal experiments in quantum circuit optimization.

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.

🔍 Explore a testbed for quantum-inspired law learning, allowing controlled and falsifiable evaluations under measurement invariants.

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