Experimental Physics PhD student working on large-scale detector data analysis, machine learning systems, and computational physics. Focused on designing and deploying scientific and ML pipelines in high-performance research environments, with emphasis on quantum information and data-driven modeling of physical systems.
Current PhD research is proprietary — projects here represent independent work.
- Quantum and computational physics (variational algorithms, Hamiltonian simulation)
- Machine learning systems engineering (PyTorch → ONNX → C++ inference pipelines)
- Scientific computing infrastructure (reproducible visualization and analysis tooling)
Variational Quantum Eigensolver implementation for hydrogen ground-state estimation with classical diagonalization benchmarking and scaling analysis.
Focus: quantum algorithms, Hamiltonian simulation, optimization landscapes, scaling behavior
End-to-end ML deployment pipeline converting trained PyTorch models into ONNX format and executing inference in C++ using ONNX Runtime.
Focus: model deployment, cross-language inference, performance-oriented ML systems
Reproducible gnuplot + LaTeX system for consistent publication-quality scientific figures across projects.
Focus: scientific visualization, automation, reproducibility
Output from characterizing VQE as a solution to the Hydrogen atom's ground state. Quantifying the minimum achievable error as a function of the number of qubits and maximum radius r in the Hamiltonian approximation
Output from the ONNX ML pipeline. Showcases: - Noisy input data to the C++ inference - The output C++ inference - The true function
Both plots were created using my gnuplot latex utilities repository.
Physics Simulation → ML Modeling → Deployment Runtime → Scientific Visualization
Python · PyTorch · Qiskit · ONNX · C++ · Eigen · CMake · Gnuplot · LaTeX · Linux
GitHub: ksalamone59