This section collects project-based materials for learning quantum computing, quantum machine learning, and related tools through realistic examples.
Project Ideas
Transformer-Based Quantum Error Mitigation
Benchmark quantum error mitigation strategies on a synthetic 3-qubit dataset: unmitigated execution, zero-noise extrapolation, a histogram MLP, and a Transformer encoder on raw shot sequences. A useful learning goal is to test whether sequence-based models learn information beyond compact histogram features.
NV-Center Quantum Sensing for Biomedicine
Simulate nanodiamond NV-center \(T_1\) relaxometry for mitochondrial free-radical conditions using Qiskit. This project can cover amplitude-damping models, inversion-recovery protocols, \(T_1\) fitting, and standard-quantum-limit \(1/\sqrt{N}\) ensemble scaling.
A Study on Quantum Neural Networks
Implement variational quantum classifiers and quantum neural networks, then compare them against classical baselines across multiple benchmarks.
HHL for Linear Regression
Implement and analyze the HHL algorithm for linear regression on quantum simulators, including numerical stability and resource requirements.
Signal Denoising with Fourier Methods
Implement Fourier-transform-based amplitude-thresholding denoising and evaluate performance on synthetic and real 1D signals.