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.
Open Source Python library for Quantum Data Encodings in Quantum ML - multi-framework support, analysis tools
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.
AI/ML Graduate Student @ ASU | Scientific Developer @ Cadence | Specializing in GenAI, CUDA, Protein Modeling & Deep Learning
A minimal hybrid Quantum–Graph Neural Network prototype
Agentic QPU calibration loop. NVIDIA Ising Calibration + Crowe Logic Foundry + Synapse-Lang + Qubit-Flow. Apache 2.0.
Repository untuk eksperimen dan latihan Quantum Machine Learning
Quantum Representation Learning with contrastive SWAP test using TensorFlow Quantum and Cirq.
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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