Classifying, auto-encoding and reverse-engineering QUBO matrices
High-performance, differentiable quantum state-vector & tensor network simulator in 100% pure JAX (no classical framework overhead). Accelerated on NVIDIA GPUs and Google Cloud TPU v6e-64/v5e VM clusters up to 37 qubits! Supported by Google's TPU Research Cloud (TRC) program.
Quantum kernel estimation with backend-matched IBM noise modeling, plus reproducible “Wigner’s friend” branch-transfer coherence-witness experiments executed on superconducting quantum hardware.
Open Source Python library for Quantum Data Encodings in Quantum ML - multi-framework support, analysis tools
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.
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.
Agentic QPU calibration loop. NVIDIA Ising Calibration + Crowe Logic Foundry + Synapse-Lang + Qubit-Flow. Apache 2.0.
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.
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
🔍 Explore a testbed for quantum-inspired law learning, allowing controlled and falsifiable evaluations under measurement invariants.
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