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)
- Built C++ ML inference system achieving 9.4M samples/sec (16× speedup) in batched vs per sample inference
- Quantified VQE error decomposition (discretization vs variational limits)
- Designed end-to-end scientific pipelines (simulation
$\rightarrow$ ML$\rightarrow$ deployment$\rightarrow$ visualization)
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Quantified VQE vs classical ground-state solutions under identical discretization by isolating variational vs discretization error contributions, showing up to 38% error is representation-bound in low-qubit regimes.
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Why this Matters: Identifies when VQE improvements should target problem representation (discretization/encoding) rather than circuit design or optimization.
Focus: quantum algorithms, Hamiltonian discretization, error decomposition, variational landscapes, scaling behavior
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C++ inference engine with pre-allocated tensor reuse and singleton session management for zero-overhead-per-call ORT deployment. Includes statistically rigorous benchmarking via Welford online variance estimation — batched inference achieves 9.4M samples/s, ~16× over sequential baseline. Engineering patterns drawn from production physics reconstruction constraints.
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Why this Matters: Demonstrates full pipeline from training in Python to an environment with highly optimized, Python-free inference; and studies the overhead implications of batched vs per sample inference.
Focus: ML deployment, C++ inference systems, performance benchmarking
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Reproducible gnuplot + LaTeX system for consistent publication-quality scientific figures across projects.
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Why this Matters: Provides a consistent visual language across all projects via a shared template and scripting layer
Focus: scientific visualization, automation, reproducibility
This heatmap shows the 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 → C++ Deployment → Scientific Visualization
Python · PyTorch · Qiskit · ONNX · C++ · Eigen · CMake · Gnuplot · LaTeX · Linux
GitHub: ksalamone59