# Final Capstone Workflow The capstone is the final proof that the course is practical rather than just well-noted. A good project should make one quantum information idea measurable, reproducible, and honest about its limits. Use the companion repo for the runnable surface: > [!info] Course code > - [capstone/README.md](https://github.com/montekkundan/quantum-code/blob/main/capstone/README.md) > - [capstone/final_report_template.md](https://github.com/montekkundan/quantum-code/blob/main/capstone/final_report_template.md) > - [notebooks/07_advanced_projects/final_capstone_workflow.ipynb](https://github.com/montekkundan/quantum-code/blob/main/notebooks/07_advanced_projects/final_capstone_workflow.ipynb) > - [scripts/run_capstone_checks.py](https://github.com/montekkundan/quantum-code/blob/main/scripts/run_capstone_checks.py) ## Required Shape Each capstone must include these parts: 1. a source-backed question from [[Source Reading Guide]] 2. one mathematical claim or invariant 3. one tested helper or circuit implementation 4. one notebook that produces the main evidence 5. one simulator baseline 6. one noise, hardware, resource-estimation, or scaling comparison 7. one written limitation section ## Project Lanes ### Bell, Randomness, And Nonlocal Games Build on [[concepts/Bell's Inequality and CHSH]], [[concepts/Nonlocal Games]], and [[concepts/Einstein-Certified Randomness]]. Minimum evidence: - classical CHSH baseline - quantum strategy estimate - no-signalling check on marginal distributions - randomness-certification discussion that states assumptions explicitly ### Hamiltonians And Noise Build on [[concepts/Hamiltonians]] and [[concepts/The Adiabatic Algorithm]]. Minimum evidence: - Hamiltonian definition and spectral check - ideal evolution result - noisy or open-system comparison - conserved quantity or norm/unitarity check where applicable ### Algorithms And Resource Limits Build on [[concepts/Quantum Query Complexity and Deutsch-Jozsa]], [[concepts/Bernstein-Vazirani and Simon's Algorithm]], [[concepts/Grover's Algorithm]], [[concepts/Quantum Fourier Transform]], or [[concepts/RSA, Period Finding, and Shor's Algorithm]]. Minimum evidence: - classical baseline - oracle or circuit definition - small-scale quantum result - scaling, query-count, or resource-estimation discussion ### Error Correction And Stabilizers Build on [[concepts/Quantum Error Correction]] and [[concepts/Stabilizer Formalism]]. Minimum evidence: - encoded logical state - syndrome table - simulated error and recovery - explanation of what the syndrome reveals and what it deliberately hides ## Grading Rubric | Criterion | Strong submission | | --- | --- | | Source alignment | Names the specific Aaronson lecture/chapter or other reference that motivated the question. | | Mathematical clarity | States the invariant, bound, or recovery rule before showing code. | | Implementation | Uses `qcourse/` helpers or adds a tested helper instead of hiding logic only in the notebook. | | Evidence | Includes simulator output and one comparison against noise, hardware, resources, or scaling. | | Interpretation | Explains what the result means and what it does not prove. | | Reproducibility | Passes tests and notebook smoke checks from the repo root. | ## Final Checklist Before calling a capstone finished: - run `uv run --extra dev pytest` - run the project notebook top-to-bottom - include a short source-reading paragraph - include a table of assumptions - include one failure mode or limitation - state whether hardware was used or whether the result is simulator-only The best capstones do not overclaim. They make a small quantum information claim precise enough that another student can rerun it, critique it, and extend it.