About

Quantum Controls & Hardware-Aware Compilation

I'm an engineering physics student at Embry-Riddle Aeronautical University (BS 2027, MS thesis track 2028). I model Hamiltonians and noise on superconducting qubits, optimize control pulses, and build the compiler infrastructure that connects circuits to hardware. Three preprints on arXiv, all hardware-validated on IQM Garnet.

What I Build

I work across the quantum compilation stack: high-level circuits (OpenQASM 3.0, C++ compiler with DAG IR and SABRE routing), through gate optimization, down to noise-robust control pulses (GRAPE with Lindblad master equation, T₁/T₂ decoherence). The through-line is closing the loop between what algorithms assume and what hardware actually does.

Key Results

  • 99.14% gate fidelity via GRAPE optimal control with Lindblad noise modeling (77× error reduction)
  • 117× speedup on GPU-accelerated quantum chemistry simulations (VQE on 4× NVIDIA H100)
  • 371 circuits analyzed end-to-end from circuit optimization through pulse simulation to hardware execution
  • 1,321 tests on QubitOS alone; 2,600+ tests across all projects
  • r = -0.74 correlation between pulse duration and infidelity (decoherence dominates gate error budgets)

Current Research

Quantum Controls & Compilers

  • QubitOS: Open-source quantum control kernel. GRAPE pulse optimizer (99.9% fidelity), Rust HAL server (gRPC), pluggable backends (QuTiP simulator, IQM Garnet hardware). 1,321 tests, CI/CD. In Development.
  • QCO-Integration: End-to-end framework connecting circuit optimization with pulse simulation for fidelity analysis. 371 circuits, hardware-validated on IQM Garnet. Submitted to ACM TQC. arXiv:2601.20871
  • QubitPulseOpt: GRAPE pulse optimization with Lindblad master equation for T₁/T₂ noise on transmon qubits. 99.14% X-gate fidelity in 20ns. 822 tests. Submitted to Quantum. arXiv:2511.12799
  • Quantum Circuit Optimizer: C++ quantum compiler. OpenQASM 3.0 parser, DAG IR, 4 optimization passes, SABRE routing. 340 tests.
  • CUDA Quantum Simulator: GPU-accelerated state vector simulator. 4.1× over CPU at 22 qubits. 156 tests.

High-Performance Computing

Machine Learning & Atmospheric Science

  • NASA GSFC Intern (Summer 2025): ML framework for cloud base height retrieval from ER-2 airborne observations. Two first-author papers pending NASA approval.

Robotics

  • AIRHOUND: Autonomous UAV pursuit system. PI and perception lead on team of 7. RF-DETR with TensorRT on Jetson (3.6ms inference, 275.7 FPS). SPIE Defense + Security 2026.

Education

Embry-Riddle Aeronautical University | Daytona Beach, FL

  • BS Engineering Physics | Expected May 2027
  • MS Engineering Physics (Thesis Track) | Expected May 2028
  • Spacecraft Instrumentation Track, Computational Mathematics Minor

Experience

NASA Goddard Space Flight Center | May – Aug 2025 OSTEM Intern, Atmospheric Remote Sensing

Technical Skills

Languages: Python, C/C++, Rust, CUDA, MATLAB, Bash Quantum: QuTiP, PennyLane, Qiskit, JAX, GRAPE Physics: Lindblad master equation, transmon Hamiltonians, T₁/T₂ noise, optimal control, error budgets HPC: CUDA, OpenMPI, H100, JIT, multi-GPU scaling ML: PyTorch, TensorFlow, scikit-learn, XGBoost Tools: Git, Docker, Linux, CI/CD, CMake, gRPC

Awards

  • Goldwater Scholarship Campus Finalist (2025)
  • SPIE Defense + Security 2026 — Accepted presentation
  • USTFCCCA Academic All-American (2024, 2025)
  • 136th at 2025 NCAA D2 Cross Country Nationals

Contact