Quantum Systems Software & Compilers
I’m a junior at Embry-Riddle Aeronautical University pursuing a BS/MS in Engineering Physics with a Spacecraft Instrumentation track and Computational Math minor (expected May 2027).
I develop hardware-aware quantum systems software (compilers + pulse optimization) to bridge quantum algorithms and noisy hardware via high-performance computing. My goal is to build deployment-ready quantum compilers for industry, not just theory papers.
Research Direction
My research prepares me for PhD work in hardware-aware quantum compilation and systems software. I’m building a full-stack quantum compilation pipeline: from high-level circuits (OpenQASM) through gate optimization and qubit routing, down to noise-robust control pulses.
Long-Term Vision
After contributing to R&D in quantum hardware-software co-design, I plan to found an open-source quantum compiler enterprise (modeled after Red Hat) to keep fundamental quantum experimentation open and non-proprietary.
Key Results
- 117x speedup on GPU-accelerated quantum chemistry simulations (VQE on 4x NVIDIA H100)
- 99.14% gate fidelity in quantum optimal control simulations using GRAPE
- R² = 0.744 for ML-based cloud retrieval at NASA GSFC, outperforming CNNs by 130%
Current Research
Quantum Compilers & Control
- QubitPulseOpt: Quantum gate optimization using GRAPE with Lindblad noise modeling. Achieved 99.14% X-gate fidelity in 20ns simulations. arXiv:2511.12799
- Quantum Circuit Optimizer: Production C++ quantum compiler with OpenQASM 3.0 parser, DAG-based IR, 4 optimization passes, and SABRE routing. 340 unit tests.
- CUDA Quantum Simulator: GPU-accelerated state vector simulator with 9 test suites. Up to 29 qubits on single GPU.
- LLVM Unroll Analyzer: Custom LLVM pass for loop analysis using Scalar Evolution.
Open Source
- Research Code Principles: Framework for building production-grade research software with AI coding agents. Includes style guides, prompting strategies, and OpenCode Context Manager tool.
High-Performance Computing
- High-Performance VQE: GPU-accelerated Variational Quantum Eigensolver achieving 117x speedup through JIT compilation, multi-GPU scaling, and MPI parallelization on ERAU’s Vega HPC cluster.
Machine Learning for Atmospheric Science
- NASA GSFC Intern (Summer 2025): Developed ML framework for cloud base height retrieval from ER-2 airborne observations. First-author preprint pending journal submission.
Robotics
- AIRHOUND UAV Pursuit System: PI and perception lead on team of 7. CV development (YOLOv8, ROS2) for autonomous drone tracking. Abstract submitted to SPIE Defense & Security 2026.
Education
Embry-Riddle Aeronautical University | Daytona Beach, FL
BS/MS Engineering Physics (Accelerated) | Expected May 2027
Spacecraft Instrumentation Track, Computational Math Minor
Experience
NASA Goddard Space Flight Center | May - Aug 2025
OSTEM Intern, Atmospheric Remote Sensing
- Developed ML framework comparing feature-based vs. image-based approaches for cloud retrieval
- First-author preprint: “Atmospheric Features Outperform Images for Cloud Base Height Retrieval”
Technical Skills
Compilers: LLVM (custom passes), OpenQASM 3.0, DAG-based IR, circuit optimization
Quantum Computing: PennyLane, QuTiP, Qiskit, JAX, GRAPE optimization
HPC: CUDA, OpenMPI, GPU acceleration (H100), JIT compilation
Machine Learning: PyTorch, TensorFlow, scikit-learn, XGBoost
Programming: Python, C/C++, MATLAB, Bash
Robotics: ROS2, PX4, NVIDIA Jetson
Awards
- Goldwater Scholarship Campus Finalist
- 136th at 2025 NCAA D2 Cross Country Nationals
- USTFCCCA Academic All-American (2024, 2025)
- ERAU Dean’s List
- NCAA Division II Cross Country & Track (2023 - Present)
Contact
- Email: rylan1012@gmail.com
- GitHub: github.com/rylanmalarchick
- LinkedIn: linkedin.com/in/rylan-malarchick