These project briefs document hands-on studies that connect quantum algorithms with realistic modeling, simulation, and evaluation questions.
Selected Project Work
Transformer-Based Quantum Error Mitigation
Benchmarked unmitigated estimation, zero-noise extrapolation, a histogram MLP, and a Transformer encoder on raw shot sequences for a synthetic three-qubit setting. A shuffle ablation showed that the Transformer learned a permutation-invariant representation rather than useful sequence order.
NV-Center Quantum Sensing for Biomedicine
Simulated nanodiamond NV-center \(T_1\) relaxometry for mitochondrial free-radical conditions using amplitude damping, inversion recovery, \(T_1\) fitting, and standard-quantum-limit ensemble scaling.
Quantum Neural Network Benchmark Study
Implemented variational quantum classifiers and compared them with classical baselines across multiple benchmark datasets, emphasizing fair preprocessing and resource-aware interpretation.
HHL for Linear Regression
Implemented and analyzed HHL-based linear regression on quantum simulators, including numerical stability, matrix conditioning, data-loading assumptions, and resource requirements.
Reproducibility Perspective
For these studies, the implementation is only one part of the result. I also examine baseline selection, simulator assumptions, computational cost, and whether conclusions remain meaningful under realistic data access and hardware constraints.