Classifying, auto-encoding and reverse-engineering QUBO matrices
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.
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
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.
A Python demo of a quantum processor calibration loop: it reads a measurement trace, asks a mock or live NVIDIA Ising Calibration client what to fix, and simulates the corrective step; no hardware has been touched.
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.
Hybrid Quantum-Classical LLM Benchmark Framework
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